{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "598b6054-6147-44b1-a3f2-35ae1f7828ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import pandas as pd\n",
    "from scipy.stats import spearmanr, pearsonr\n",
    "from statsmodels.sandbox.stats.multicomp import multipletests\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from skbio.stats.composition import clr\n",
    "from matplotlib_venn import venn3\n",
    "\n",
    "sns.set_context('talk')\n",
    "sns.set(rc={\"figure.dpi\":150, 'savefig.dpi':300})\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "def p_adjust(pvalues, method='fdr_bh'):\n",
    "    res = multipletests(pvalues, method=method)\n",
    "    return np.array(res[1], dtype=float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b4d9bb0-fa06-4898-aca1-d72c51431e2c",
   "metadata": {},
   "source": [
    "# Metabolomics correlations\n",
    "##### 7/18/22\n",
    "##### Michael Shaffer\n",
    "##### Merck ESC, Sys bio group\n",
    "\n",
    "After some success with correlating KOs with continuous titer we decided to use the same approach on the metabolomics data. This data table came from Tom directly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "396ba13c-ce03-4289-8e8a-5a1ebb4e0f3e",
   "metadata": {},
   "source": [
    "## Read in data\n",
    "\n",
    "Read in the excel table from Tom and fill out empty cells with zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae77ebe4-ab21-4f4b-b6f7-7023b8acf0a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GCDCA</th>\n",
       "      <th>GDCA</th>\n",
       "      <th>GHDCA or GUDCA</th>\n",
       "      <th>CA</th>\n",
       "      <th>TCDCA</th>\n",
       "      <th>TCA</th>\n",
       "      <th>CDCA</th>\n",
       "      <th>7oxoCA</th>\n",
       "      <th>TUDCA</th>\n",
       "      <th>UDCA or HDCA</th>\n",
       "      <th>...</th>\n",
       "      <th>Uracil</th>\n",
       "      <th>Adenosine</th>\n",
       "      <th>Phenaceturic acid</th>\n",
       "      <th>N-Acetyl D-galactosamine</th>\n",
       "      <th>Pyridoxal hydrochloride</th>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <th>Vanillic acid</th>\n",
       "      <th>Thymine</th>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <th>Homocitrate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Compound name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P103_V12_05052020</th>\n",
       "      <td>13165.576990</td>\n",
       "      <td>6486.205847</td>\n",
       "      <td>1037.427698</td>\n",
       "      <td>877.477368</td>\n",
       "      <td>842.441112</td>\n",
       "      <td>428.399288</td>\n",
       "      <td>412.663454</td>\n",
       "      <td>6.309238</td>\n",
       "      <td>26.103778</td>\n",
       "      <td>37.617317</td>\n",
       "      <td>...</td>\n",
       "      <td>571</td>\n",
       "      <td>112</td>\n",
       "      <td>45</td>\n",
       "      <td>90</td>\n",
       "      <td>426</td>\n",
       "      <td>1223</td>\n",
       "      <td>6865</td>\n",
       "      <td>549</td>\n",
       "      <td>217</td>\n",
       "      <td>3044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P106_V9_04022019</th>\n",
       "      <td>5324.081005</td>\n",
       "      <td>615.267690</td>\n",
       "      <td>1431.812977</td>\n",
       "      <td>536.910350</td>\n",
       "      <td>477.807838</td>\n",
       "      <td>761.254736</td>\n",
       "      <td>55.773899</td>\n",
       "      <td>4.142229</td>\n",
       "      <td>91.815489</td>\n",
       "      <td>19.861706</td>\n",
       "      <td>...</td>\n",
       "      <td>400</td>\n",
       "      <td>368</td>\n",
       "      <td>206</td>\n",
       "      <td>217</td>\n",
       "      <td>201</td>\n",
       "      <td>372</td>\n",
       "      <td>53</td>\n",
       "      <td>230</td>\n",
       "      <td>216</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P107_A1_04152019</th>\n",
       "      <td>11967.195030</td>\n",
       "      <td>3.048386</td>\n",
       "      <td>8876.579286</td>\n",
       "      <td>884.309058</td>\n",
       "      <td>2720.613568</td>\n",
       "      <td>3492.197205</td>\n",
       "      <td>106.185518</td>\n",
       "      <td>7.807234</td>\n",
       "      <td>664.956760</td>\n",
       "      <td>113.460800</td>\n",
       "      <td>...</td>\n",
       "      <td>594</td>\n",
       "      <td>112</td>\n",
       "      <td>8</td>\n",
       "      <td>330</td>\n",
       "      <td>102</td>\n",
       "      <td>9</td>\n",
       "      <td>87</td>\n",
       "      <td>55</td>\n",
       "      <td>177</td>\n",
       "      <td>6952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V9_04022019</th>\n",
       "      <td>117.128627</td>\n",
       "      <td>-0.240781</td>\n",
       "      <td>116.125243</td>\n",
       "      <td>29.790592</td>\n",
       "      <td>6.916959</td>\n",
       "      <td>5.775257</td>\n",
       "      <td>1.574994</td>\n",
       "      <td>0.418815</td>\n",
       "      <td>1.671431</td>\n",
       "      <td>4.996911</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>226</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>249</td>\n",
       "      <td>104</td>\n",
       "      <td>64</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V12_05212020</th>\n",
       "      <td>16651.224940</td>\n",
       "      <td>1707.969274</td>\n",
       "      <td>3575.590198</td>\n",
       "      <td>1277.281488</td>\n",
       "      <td>2356.663104</td>\n",
       "      <td>1109.351448</td>\n",
       "      <td>204.781749</td>\n",
       "      <td>41.696046</td>\n",
       "      <td>216.707453</td>\n",
       "      <td>107.961759</td>\n",
       "      <td>...</td>\n",
       "      <td>375</td>\n",
       "      <td>184</td>\n",
       "      <td>205</td>\n",
       "      <td>141</td>\n",
       "      <td>253</td>\n",
       "      <td>702</td>\n",
       "      <td>298</td>\n",
       "      <td>196</td>\n",
       "      <td>196</td>\n",
       "      <td>3468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          GCDCA         GDCA  GHDCA or GUDCA           CA  \\\n",
       "Compound name                                                               \n",
       "P103_V12_05052020  13165.576990  6486.205847     1037.427698   877.477368   \n",
       "P106_V9_04022019    5324.081005   615.267690     1431.812977   536.910350   \n",
       "P107_A1_04152019   11967.195030     3.048386     8876.579286   884.309058   \n",
       "P108_V9_04022019     117.128627    -0.240781      116.125243    29.790592   \n",
       "P108_V12_05212020  16651.224940  1707.969274     3575.590198  1277.281488   \n",
       "\n",
       "                         TCDCA          TCA        CDCA     7oxoCA  \\\n",
       "Compound name                                                        \n",
       "P103_V12_05052020   842.441112   428.399288  412.663454   6.309238   \n",
       "P106_V9_04022019    477.807838   761.254736   55.773899   4.142229   \n",
       "P107_A1_04152019   2720.613568  3492.197205  106.185518   7.807234   \n",
       "P108_V9_04022019      6.916959     5.775257    1.574994   0.418815   \n",
       "P108_V12_05212020  2356.663104  1109.351448  204.781749  41.696046   \n",
       "\n",
       "                        TUDCA  UDCA or HDCA  ...  Uracil  Adenosine  \\\n",
       "Compound name                                ...                      \n",
       "P103_V12_05052020   26.103778     37.617317  ...     571        112   \n",
       "P106_V9_04022019    91.815489     19.861706  ...     400        368   \n",
       "P107_A1_04152019   664.956760    113.460800  ...     594        112   \n",
       "P108_V9_04022019     1.671431      4.996911  ...      60        226   \n",
       "P108_V12_05212020  216.707453    107.961759  ...     375        184   \n",
       "\n",
       "                   Phenaceturic acid  N-Acetyl D-galactosamine  \\\n",
       "Compound name                                                    \n",
       "P103_V12_05052020                 45                        90   \n",
       "P106_V9_04022019                 206                       217   \n",
       "P107_A1_04152019                   8                       330   \n",
       "P108_V9_04022019                  11                        16   \n",
       "P108_V12_05212020                205                       141   \n",
       "\n",
       "                   Pyridoxal hydrochloride  2-Deoxyuridine  Vanillic acid  \\\n",
       "Compound name                                                               \n",
       "P103_V12_05052020                      426            1223           6865   \n",
       "P106_V9_04022019                       201             372             53   \n",
       "P107_A1_04152019                       102               9             87   \n",
       "P108_V9_04022019                       249             104             64   \n",
       "P108_V12_05212020                      253             702            298   \n",
       "\n",
       "                   Thymine  Nicotinic acid  Homocitrate  \n",
       "Compound name                                            \n",
       "P103_V12_05052020      549             217         3044  \n",
       "P106_V9_04022019       230             216          109  \n",
       "P107_A1_04152019        55             177         6952  \n",
       "P108_V9_04022019        18              24           36  \n",
       "P108_V12_05212020      196             196         3468  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data = pd.read_excel('../../data/metabolomics_abunds.xlsx', index_col=0).fillna(0)\n",
    "metab_data = metab_data.drop('2-Methyl-1-butanol', axis=1) # drop 2-methyl-1-butanol\n",
    "metab_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6fcdc66f-562b-44ef-aa90-68f9a2a511f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(metab_data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1beeb59b-e8eb-4320-8ead-baff4dc6d0de",
   "metadata": {},
   "source": [
    "111 total compounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e25e7c30-c055-461b-94fc-cf83819b00cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(metab_data < 0).sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96bf9ccc-21da-4366-9d65-c42295ef8d3d",
   "metadata": {},
   "source": [
    "There are only 34 negative values in the data set so not much to worry about."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "afa2e02b-9851-41bb-be46-7da0a98b70f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        Compound name    \n",
       "GDCA    P108_V9_04022019    -0.240781\n",
       "        P134_V9_12062019    -3.462650\n",
       "        P136_V9_01092020    -3.398780\n",
       "        P136_V12_01042021   -0.059861\n",
       "        P202_V12_02252020   -1.708202\n",
       "        P212_V5_05232018    -2.539283\n",
       "        P215_V9_03292019    -4.984036\n",
       "        P215_V12_03302020   -2.424750\n",
       "        P217_V5_06122018    -1.613075\n",
       "        P217_V9_04122019    -0.517431\n",
       "        P218_V5_06062018    -0.427326\n",
       "        P220_V5_06132018    -0.218075\n",
       "        P224_V9_05292019    -0.396865\n",
       "        P229_V5_07022018    -0.664653\n",
       "        P235_V5_08152018    -0.203266\n",
       "        P237_V9_06242019    -0.407863\n",
       "        P239_V5_08292018    -0.942639\n",
       "        P246_V10_10032019   -1.541833\n",
       "        P250_V5_11062018    -1.424605\n",
       "        P250_V9_09092019    -2.380330\n",
       "        P252_V9_09102019    -1.033160\n",
       "        P256_V9_10082018    -0.539685\n",
       "        P258_V5_01032019    -1.193836\n",
       "        P260_V9_11072019    -7.410659\n",
       "        P261_V5_01032019    -2.489533\n",
       "        P263_V9_11262019    -2.335442\n",
       "7oxoCA  P253_V12_09242020   -2.383277\n",
       "DCA     P115_V12_05282020   -0.036132\n",
       "        P136_V9_01092020    -0.927228\n",
       "        P233_V5_07182018    -0.085570\n",
       "        P234_V5_08072018    -1.335333\n",
       "        P236_V5_08142018    -2.929766\n",
       "        P238_V9_06242019    -0.044959\n",
       "        P252_V9_09102019    -1.587543\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data.unstack()[metab_data.unstack() < 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38c1a43f-663f-4866-b3ef-cd701aa1ed47",
   "metadata": {},
   "source": [
    "There are negative numbers in the data set. Checked with Tom and he said that these can be treated as zeros. So do that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a69bd109-0485-4dd4-9b7b-b39fab00e911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace negative numbers with 0 as Tom said in an email\n",
    "metab_data[metab_data < 0] = 0\n",
    "(metab_data == 0).sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6925d1d5-9370-4855-af1b-94b3a63700e7",
   "metadata": {},
   "source": [
    "Relative abundance normalized version of table is calculated by summing rows and dividing column values by totals. Makes it so that values are percent abundances of sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "758d39b1-abdb-407b-b02c-c0792f1d0d7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GCDCA</th>\n",
       "      <th>GDCA</th>\n",
       "      <th>GHDCA or GUDCA</th>\n",
       "      <th>CA</th>\n",
       "      <th>TCDCA</th>\n",
       "      <th>TCA</th>\n",
       "      <th>CDCA</th>\n",
       "      <th>7oxoCA</th>\n",
       "      <th>TUDCA</th>\n",
       "      <th>UDCA or HDCA</th>\n",
       "      <th>...</th>\n",
       "      <th>Uracil</th>\n",
       "      <th>Adenosine</th>\n",
       "      <th>Phenaceturic acid</th>\n",
       "      <th>N-Acetyl D-galactosamine</th>\n",
       "      <th>Pyridoxal hydrochloride</th>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <th>Vanillic acid</th>\n",
       "      <th>Thymine</th>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <th>Homocitrate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Compound name</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>P103_V12_05052020</th>\n",
       "      <td>0.001680</td>\n",
       "      <td>8.275956e-04</td>\n",
       "      <td>0.000132</td>\n",
       "      <td>0.000112</td>\n",
       "      <td>0.000107</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>5.265304e-05</td>\n",
       "      <td>8.050157e-07</td>\n",
       "      <td>3.330664e-06</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000876</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.000388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P106_V9_04022019</th>\n",
       "      <td>0.000847</td>\n",
       "      <td>9.783810e-05</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>8.869005e-06</td>\n",
       "      <td>6.586853e-07</td>\n",
       "      <td>1.460024e-05</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P107_A1_04152019</th>\n",
       "      <td>0.002120</td>\n",
       "      <td>5.401497e-07</td>\n",
       "      <td>0.001573</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>0.000619</td>\n",
       "      <td>1.881523e-05</td>\n",
       "      <td>1.383380e-06</td>\n",
       "      <td>1.178250e-04</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.001232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V9_04022019</th>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>8.751132e-07</td>\n",
       "      <td>2.327061e-07</td>\n",
       "      <td>9.286966e-07</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>0.000058</td>\n",
       "      <td>0.000036</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V12_05212020</th>\n",
       "      <td>0.002614</td>\n",
       "      <td>2.681638e-04</td>\n",
       "      <td>0.000561</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>3.215225e-05</td>\n",
       "      <td>6.546587e-06</td>\n",
       "      <td>3.402467e-05</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.000545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      GCDCA          GDCA  GHDCA or GUDCA        CA     TCDCA  \\\n",
       "Compound name                                                                   \n",
       "P103_V12_05052020  0.001680  8.275956e-04        0.000132  0.000112  0.000107   \n",
       "P106_V9_04022019   0.000847  9.783810e-05        0.000228  0.000085  0.000076   \n",
       "P107_A1_04152019   0.002120  5.401497e-07        0.001573  0.000157  0.000482   \n",
       "P108_V9_04022019   0.000065  0.000000e+00        0.000065  0.000017  0.000004   \n",
       "P108_V12_05212020  0.002614  2.681638e-04        0.000561  0.000201  0.000370   \n",
       "\n",
       "                        TCA          CDCA        7oxoCA         TUDCA  \\\n",
       "Compound name                                                           \n",
       "P103_V12_05052020  0.000055  5.265304e-05  8.050157e-07  3.330664e-06   \n",
       "P106_V9_04022019   0.000121  8.869005e-06  6.586853e-07  1.460024e-05   \n",
       "P107_A1_04152019   0.000619  1.881523e-05  1.383380e-06  1.178250e-04   \n",
       "P108_V9_04022019   0.000003  8.751132e-07  2.327061e-07  9.286966e-07   \n",
       "P108_V12_05212020  0.000174  3.215225e-05  6.546587e-06  3.402467e-05   \n",
       "\n",
       "                   UDCA or HDCA  ...    Uracil  Adenosine  Phenaceturic acid  \\\n",
       "Compound name                    ...                                           \n",
       "P103_V12_05052020      0.000005  ...  0.000073   0.000014           0.000006   \n",
       "P106_V9_04022019       0.000003  ...  0.000064   0.000059           0.000033   \n",
       "P107_A1_04152019       0.000020  ...  0.000105   0.000020           0.000001   \n",
       "P108_V9_04022019       0.000003  ...  0.000033   0.000126           0.000006   \n",
       "P108_V12_05212020      0.000017  ...  0.000059   0.000029           0.000032   \n",
       "\n",
       "                   N-Acetyl D-galactosamine  Pyridoxal hydrochloride  \\\n",
       "Compound name                                                          \n",
       "P103_V12_05052020                  0.000011                 0.000054   \n",
       "P106_V9_04022019                   0.000035                 0.000032   \n",
       "P107_A1_04152019                   0.000058                 0.000018   \n",
       "P108_V9_04022019                   0.000009                 0.000138   \n",
       "P108_V12_05212020                  0.000022                 0.000040   \n",
       "\n",
       "                   2-Deoxyuridine  Vanillic acid   Thymine  Nicotinic acid  \\\n",
       "Compound name                                                                \n",
       "P103_V12_05052020        0.000156       0.000876  0.000070        0.000028   \n",
       "P106_V9_04022019         0.000059       0.000008  0.000037        0.000034   \n",
       "P107_A1_04152019         0.000002       0.000015  0.000010        0.000031   \n",
       "P108_V9_04022019         0.000058       0.000036  0.000010        0.000013   \n",
       "P108_V12_05212020        0.000110       0.000047  0.000031        0.000031   \n",
       "\n",
       "                   Homocitrate  \n",
       "Compound name                   \n",
       "P103_V12_05052020     0.000388  \n",
       "P106_V9_04022019      0.000017  \n",
       "P107_A1_04152019      0.001232  \n",
       "P108_V9_04022019      0.000020  \n",
       "P108_V12_05212020     0.000545  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_rel = metab_data.div(metab_data.sum(axis=1), axis=0)\n",
    "metab_data_rel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcaeb76-e9a5-4c12-93b9-3d8c26568a4c",
   "metadata": {},
   "source": [
    "CLR normalized version of table is calculated using CLR (centered log ratio transformation) which controls for compositionality. This is done using the scikit-bio package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0464f150-61f1-49ff-b44b-2f7e29552f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "metab_data_clr = pd.DataFrame(clr(metab_data + .001), index=metab_data.index, columns=metab_data.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d27a752-cf90-4bdf-b80d-81f5e723401e",
   "metadata": {},
   "source": [
    "Build simple metadata table based on the sample names from the metabolomics table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63e7c570-f966-4e62-a8ec-6638352c0044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BabyN</th>\n",
       "      <th>VisitCode</th>\n",
       "      <th>VisitDate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SampleID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P103_V12_05052020</th>\n",
       "      <td>103</td>\n",
       "      <td>V12</td>\n",
       "      <td>05052020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P106_V9_04022019</th>\n",
       "      <td>106</td>\n",
       "      <td>V9</td>\n",
       "      <td>04022019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P107_A1_04152019</th>\n",
       "      <td>107</td>\n",
       "      <td>A1</td>\n",
       "      <td>04152019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V9_04022019</th>\n",
       "      <td>108</td>\n",
       "      <td>V9</td>\n",
       "      <td>04022019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P108_V12_05212020</th>\n",
       "      <td>108</td>\n",
       "      <td>V12</td>\n",
       "      <td>05212020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   BabyN VisitCode VisitDate\n",
       "SampleID                                    \n",
       "P103_V12_05052020    103       V12  05052020\n",
       "P106_V9_04022019     106        V9  04022019\n",
       "P107_A1_04152019     107        A1  04152019\n",
       "P108_V9_04022019     108        V9  04022019\n",
       "P108_V12_05212020    108       V12  05212020"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_rows = [[i, int(i.split('_')[0].strip('P')), i.split('_')[1], i.split('_')[-1]] for i in metab_data.index]\n",
    "meta_base = pd.DataFrame(meta_rows, columns=['SampleID', 'BabyN', 'VisitCode', 'VisitDate']).set_index('SampleID')\n",
    "meta_base.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "949d7209-89e7-4db9-afc6-1ccc4c674bf4",
   "metadata": {},
   "source": [
    "Connect sample data to 1 yr titer data via the baby numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5b53321-c5d2-4289-9b6c-3e9a6a4f7fe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PT</th>\n",
       "      <th>Dip</th>\n",
       "      <th>FHA</th>\n",
       "      <th>PRN</th>\n",
       "      <th>TET</th>\n",
       "      <th>PRP (Hib)</th>\n",
       "      <th>PCV ST1</th>\n",
       "      <th>PCV ST3</th>\n",
       "      <th>PCV ST4</th>\n",
       "      <th>PCV ST5</th>\n",
       "      <th>...</th>\n",
       "      <th>median_mmNorm</th>\n",
       "      <th>median_mmNorm_DTAPHib</th>\n",
       "      <th>median_mmNorm_PCV</th>\n",
       "      <th>PT_protected</th>\n",
       "      <th>Dip_protected</th>\n",
       "      <th>FHA_protected</th>\n",
       "      <th>PRN_protected</th>\n",
       "      <th>TET_protected</th>\n",
       "      <th>PRP (Hib)_protected</th>\n",
       "      <th>VR_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>47.0</td>\n",
       "      <td>1.54</td>\n",
       "      <td>186.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>3.60</td>\n",
       "      <td>6.18</td>\n",
       "      <td>111.189953</td>\n",
       "      <td>46.960230</td>\n",
       "      <td>79.307349</td>\n",
       "      <td>82.640316</td>\n",
       "      <td>...</td>\n",
       "      <td>0.190854</td>\n",
       "      <td>0.326317</td>\n",
       "      <td>0.146465</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>31.0</td>\n",
       "      <td>1.21</td>\n",
       "      <td>34.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2.12</td>\n",
       "      <td>4.15</td>\n",
       "      <td>36.929565</td>\n",
       "      <td>31.828768</td>\n",
       "      <td>82.189284</td>\n",
       "      <td>108.892504</td>\n",
       "      <td>...</td>\n",
       "      <td>0.168430</td>\n",
       "      <td>0.172291</td>\n",
       "      <td>0.164958</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>37.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>15.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.45</td>\n",
       "      <td>13.16</td>\n",
       "      <td>85.710344</td>\n",
       "      <td>58.493770</td>\n",
       "      <td>135.699571</td>\n",
       "      <td>416.070794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.298568</td>\n",
       "      <td>0.237230</td>\n",
       "      <td>0.358166</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.23</td>\n",
       "      <td>22.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>14.69</td>\n",
       "      <td>118.861585</td>\n",
       "      <td>48.999537</td>\n",
       "      <td>92.404780</td>\n",
       "      <td>179.132640</td>\n",
       "      <td>...</td>\n",
       "      <td>0.358315</td>\n",
       "      <td>0.085661</td>\n",
       "      <td>0.468588</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>146.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>37.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>2.70</td>\n",
       "      <td>18.61</td>\n",
       "      <td>56.092263</td>\n",
       "      <td>17.140494</td>\n",
       "      <td>82.645151</td>\n",
       "      <td>196.584328</td>\n",
       "      <td>...</td>\n",
       "      <td>0.221124</td>\n",
       "      <td>0.298591</td>\n",
       "      <td>0.221124</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PT   Dip    FHA    PRN   TET  PRP (Hib)     PCV ST1    PCV ST3  \\\n",
       "103   47.0  1.54  186.0  326.0  3.60       6.18  111.189953  46.960230   \n",
       "108   31.0  1.21   34.0   43.0  2.12       4.15   36.929565  31.828768   \n",
       "110   37.0  0.62   15.0   85.0  3.45      13.16   85.710344  58.493770   \n",
       "112   11.0  0.23   22.0   68.0  0.79      14.69  118.861585  48.999537   \n",
       "113  146.0  0.38   37.0  137.0  2.70      18.61   56.092263  17.140494   \n",
       "\n",
       "        PCV ST4     PCV ST5  ...  median_mmNorm  median_mmNorm_DTAPHib  \\\n",
       "103   79.307349   82.640316  ...       0.190854               0.326317   \n",
       "108   82.189284  108.892504  ...       0.168430               0.172291   \n",
       "110  135.699571  416.070794  ...       0.298568               0.237230   \n",
       "112   92.404780  179.132640  ...       0.358315               0.085661   \n",
       "113   82.645151  196.584328  ...       0.221124               0.298591   \n",
       "\n",
       "     median_mmNorm_PCV  PT_protected  Dip_protected  FHA_protected  \\\n",
       "103           0.146465          True           True           True   \n",
       "108           0.164958          True           True           True   \n",
       "110           0.358166          True           True           True   \n",
       "112           0.468588          True           True           True   \n",
       "113           0.221124          True           True           True   \n",
       "\n",
       "     PRN_protected  TET_protected  PRP (Hib)_protected  VR_group  \n",
       "103           True           True                 True       NVR  \n",
       "108           True           True                 True       NVR  \n",
       "110           True           True                 True       NVR  \n",
       "112           True           True                 True       NVR  \n",
       "113           True           True                 True       NVR  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titer_data = pd.read_csv('../../data/vaccine_response/vaccine_response_y2.tsv', sep='\\t', index_col=0)\n",
    "titer_data.index = [int(i.split('Baby')[-1]) for i in titer_data.index]\n",
    "titer_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c54eec5d-efc0-401d-bc27-efc2ce77a949",
   "metadata": {},
   "source": [
    "Use BabyN to connect compound abundance to titer values since there is only one 2 month sample and one set of 1 year titer values per baby."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a7b806f-edf7-481a-8870-2e5ccd59a03c",
   "metadata": {},
   "outputs": [
    {
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       "                     PT   Dip    FHA    PRN   TET PRP (Hib)     PCV ST1  \\\n",
       "P103_V12_05052020  47.0  1.54  186.0  326.0   3.6      6.18  111.189953   \n",
       "P108_V9_04022019   31.0  1.21   34.0   43.0  2.12      4.15   36.929565   \n",
       "P108_V12_05212020  31.0  1.21   34.0   43.0  2.12      4.15   36.929565   \n",
       "P110_V9_05172019   37.0  0.62   15.0   85.0  3.45     13.16   85.710344   \n",
       "P110_V12_05132020  37.0  0.62   15.0   85.0  3.45     13.16   85.710344   \n",
       "\n",
       "                     PCV ST3     PCV ST4     PCV ST5  ... median_mmNorm  \\\n",
       "P103_V12_05052020   46.96023   79.307349   82.640316  ...      0.190854   \n",
       "P108_V9_04022019   31.828768   82.189284  108.892504  ...       0.16843   \n",
       "P108_V12_05212020  31.828768   82.189284  108.892504  ...       0.16843   \n",
       "P110_V9_05172019    58.49377  135.699571  416.070794  ...      0.298568   \n",
       "P110_V12_05132020   58.49377  135.699571  416.070794  ...      0.298568   \n",
       "\n",
       "                  median_mmNorm_DTAPHib median_mmNorm_PCV PT_protected  \\\n",
       "P103_V12_05052020              0.326317          0.146465         True   \n",
       "P108_V9_04022019               0.172291          0.164958         True   \n",
       "P108_V12_05212020              0.172291          0.164958         True   \n",
       "P110_V9_05172019                0.23723          0.358166         True   \n",
       "P110_V12_05132020               0.23723          0.358166         True   \n",
       "\n",
       "                  Dip_protected FHA_protected PRN_protected TET_protected  \\\n",
       "P103_V12_05052020          True          True          True          True   \n",
       "P108_V9_04022019           True          True          True          True   \n",
       "P108_V12_05212020          True          True          True          True   \n",
       "P110_V9_05172019           True          True          True          True   \n",
       "P110_V12_05132020          True          True          True          True   \n",
       "\n",
       "                  PRP (Hib)_protected VR_group  \n",
       "P103_V12_05052020                True      NVR  \n",
       "P108_V9_04022019                 True      NVR  \n",
       "P108_V12_05212020                True      NVR  \n",
       "P110_V9_05172019                 True      NVR  \n",
       "P110_V12_05132020                True      NVR  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "per_sample_titer_data = pd.DataFrame({sample: titer_data.loc[i] for sample, i in meta_base['BabyN'].iteritems() if i in titer_data.index}).transpose()\n",
    "per_sample_titer_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b4cf895-d716-479f-94bd-66fb124d9230",
   "metadata": {},
   "source": [
    "Merge sample metadata and titer data, filter out samples without one year titers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "20f8a3a2-43fd-4ba8-9b87-9f5da5d6dd72",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>P103_V12_05052020</th>\n",
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       "    <tr>\n",
       "      <th>P108_V9_04022019</th>\n",
       "      <td>108</td>\n",
       "      <td>V9</td>\n",
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       "    <tr>\n",
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       "      <td>NVR</td>\n",
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       "    <tr>\n",
       "      <th>P110_V9_05172019</th>\n",
       "      <td>110</td>\n",
       "      <td>V9</td>\n",
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       "      <td>37.0</td>\n",
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       "      <td>85.0</td>\n",
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       "    <tr>\n",
       "      <th>P110_V12_05132020</th>\n",
       "      <td>110</td>\n",
       "      <td>V12</td>\n",
       "      <td>05132020</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.62</td>\n",
       "      <td>15.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>3.45</td>\n",
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       "      <td>85.710344</td>\n",
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      ],
      "text/plain": [
       "                   BabyN VisitCode VisitDate    PT   Dip    FHA    PRN   TET  \\\n",
       "P103_V12_05052020    103       V12  05052020  47.0  1.54  186.0  326.0   3.6   \n",
       "P108_V9_04022019     108        V9  04022019  31.0  1.21   34.0   43.0  2.12   \n",
       "P108_V12_05212020    108       V12  05212020  31.0  1.21   34.0   43.0  2.12   \n",
       "P110_V9_05172019     110        V9  05172019  37.0  0.62   15.0   85.0  3.45   \n",
       "P110_V12_05132020    110       V12  05132020  37.0  0.62   15.0   85.0  3.45   \n",
       "\n",
       "                  PRP (Hib)     PCV ST1  ... median_mmNorm  \\\n",
       "P103_V12_05052020      6.18  111.189953  ...      0.190854   \n",
       "P108_V9_04022019       4.15   36.929565  ...       0.16843   \n",
       "P108_V12_05212020      4.15   36.929565  ...       0.16843   \n",
       "P110_V9_05172019      13.16   85.710344  ...      0.298568   \n",
       "P110_V12_05132020     13.16   85.710344  ...      0.298568   \n",
       "\n",
       "                  median_mmNorm_DTAPHib median_mmNorm_PCV PT_protected  \\\n",
       "P103_V12_05052020              0.326317          0.146465         True   \n",
       "P108_V9_04022019               0.172291          0.164958         True   \n",
       "P108_V12_05212020              0.172291          0.164958         True   \n",
       "P110_V9_05172019                0.23723          0.358166         True   \n",
       "P110_V12_05132020               0.23723          0.358166         True   \n",
       "\n",
       "                  Dip_protected FHA_protected PRN_protected TET_protected  \\\n",
       "P103_V12_05052020          True          True          True          True   \n",
       "P108_V9_04022019           True          True          True          True   \n",
       "P108_V12_05212020          True          True          True          True   \n",
       "P110_V9_05172019           True          True          True          True   \n",
       "P110_V12_05132020          True          True          True          True   \n",
       "\n",
       "                  PRP (Hib)_protected VR_group  \n",
       "P103_V12_05052020                True      NVR  \n",
       "P108_V9_04022019                 True      NVR  \n",
       "P108_V12_05212020                True      NVR  \n",
       "P110_V9_05172019                 True      NVR  \n",
       "P110_V12_05132020                True      NVR  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta = pd.concat([meta_base, per_sample_titer_data], axis=1)\n",
    "meta = meta.loc[~pd.isna(meta['VR_group'])]\n",
    "meta.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf45137b-050f-4ab9-89b1-bbe6296846b9",
   "metadata": {},
   "source": [
    "Find shared samples between the metadata and metabolomics data. Filter metadata to match the metabolomics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d22833fb-4d09-4de6-a803-5662cdf76539",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(120, 51)\n"
     ]
    }
   ],
   "source": [
    "in_both = list(set(meta.index) & set(metab_data.index))\n",
    "meta_matched = meta.loc[in_both]\n",
    "print(meta_matched.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5c6f28aa-5692-4347-a4d5-072a5d3e2785",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V12    49\n",
       "V9     37\n",
       "V5     28\n",
       "V10     3\n",
       "A1      1\n",
       "V6      1\n",
       "A3      1\n",
       "Name: VisitCode, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_matched['VisitCode'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55a439b0-b9da-4b27-afcd-caca8dff4d4e",
   "metadata": {},
   "source": [
    "Plurality of samples from 1 year. Many from 2 months and 2 years as well. We will focus on two months (V5) and 1 year (V9)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc99daf9-3980-4c54-a718-e84635f9fa20",
   "metadata": {},
   "source": [
    "## Do correlation with un-normalized data\n",
    "\n",
    "First take the \"raw\" data (not further normalized from what Tom gave me) and correlate each feature with the mei."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4751f63-b098-4ef0-8b56-eeb683a12052",
   "metadata": {},
   "source": [
    "### 2 months (V5) correlations\n",
    "\n",
    "First focus on the 2 month time points.\n",
    "\n",
    "We are using spearman for all of these correlations and multiple testing correction with Benjamini-Hochberg."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "65cbac06-e901-4ba4-b97b-bdc4ac6ebbae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111 4 0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>0.448276</td>\n",
       "      <td>0.016738</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>0.430820</td>\n",
       "      <td>0.022096</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uridine</th>\n",
       "      <td>-0.391352</td>\n",
       "      <td>0.039457</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hydroxyphenyllactate</th>\n",
       "      <td>-0.389710</td>\n",
       "      <td>0.040366</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate</th>\n",
       "      <td>0.367269</td>\n",
       "      <td>0.054530</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <td>-0.357417</td>\n",
       "      <td>0.061865</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Carbamoyl-DL-aspartic acid</th>\n",
       "      <td>-0.343186</td>\n",
       "      <td>0.073789</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thymine</th>\n",
       "      <td>-0.334793</td>\n",
       "      <td>0.081605</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melibiose</th>\n",
       "      <td>0.327905</td>\n",
       "      <td>0.088481</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.319650</td>\n",
       "      <td>0.097289</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <td>-0.317777</td>\n",
       "      <td>0.099375</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Aspartic Acid</th>\n",
       "      <td>-0.317460</td>\n",
       "      <td>0.099732</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>0.292830</td>\n",
       "      <td>0.130474</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deoxycytidine 5-triphosphate</th>\n",
       "      <td>0.270936</td>\n",
       "      <td>0.163155</td>\n",
       "      <td>0.914287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   rho   p_value     p_adj\n",
       "5-HIAA                        0.448276  0.016738  0.914287\n",
       "Guanosine                     0.430820  0.022096  0.914287\n",
       "Uridine                      -0.391352  0.039457  0.914287\n",
       "hydroxyphenyllactate         -0.389710  0.040366  0.914287\n",
       "p-cresol sulfate              0.367269  0.054530  0.914287\n",
       "Nicotinic acid               -0.357417  0.061865  0.914287\n",
       "N-Carbamoyl-DL-aspartic acid -0.343186  0.073789  0.914287\n",
       "Thymine                      -0.334793  0.081605  0.914287\n",
       "Melibiose                     0.327905  0.088481  0.914287\n",
       "serotonin                     0.319650  0.097289  0.914287\n",
       "2-Deoxyuridine               -0.317777  0.099375  0.914287\n",
       "L-Aspartic Acid              -0.317460  0.099732  0.914287\n",
       "Inosine                       0.292830  0.130474  0.914287\n",
       "Deoxycytidine 5-triphosphate  0.270936  0.163155  0.914287"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_v5 = meta_matched.query(\"VisitCode == 'V5'\")\n",
    "metab_data_v5 = metab_data.loc[meta_v5.index]\n",
    "# metab_data_v5 = metab_data_v5[(metab_data_v5 > 0).sum(axis=0) > metab_data_v5.shape[0]*.2]\n",
    "v5_correlations = metab_data_v5.apply(spearmanr, b=meta_v5['median_mmNorm']).transpose()\n",
    "v5_correlations.columns = ['rho', 'p_value']\n",
    "v5_correlations['p_adj'] = p_adjust(v5_correlations['p_value'])\n",
    "v5_correlations = v5_correlations.sort_values('p_value')\n",
    "print(len(v5_correlations), len(v5_correlations.query('p_value < .05')), len(v5_correlations.query('p_adj < .05')))\n",
    "v5_correlations.head(14)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ffee2da-8c7f-4883-8f9b-fe0d608c4464",
   "metadata": {},
   "source": [
    "13 compounds with raw p-values less than .05 and 1 compound with adjusted p-value less than .05.\n",
    "\n",
    "Now save correlations to file."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f90f9f6-3031-4cd6-9af2-7efc68509e21",
   "metadata": {},
   "source": [
    "#### Plot phenylpyruvic acid result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2fcd2781-197c-40f7-8132-351b27c49eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.scatterplot(x='Phenylpyruvic acid', y='median_mmNorm', hue='VisitCode',\n",
    "                   data=pd.concat([metab_data.loc[meta_matched.index], meta_matched], axis=1).query(\"VisitCode == 'V5'\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bac219f-578b-4bbe-81fc-97e1968941d5",
   "metadata": {},
   "source": [
    "Plot log phenylpyruvic acid abundance vs median normalized titer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d3f4cc45-0c44-4332-9f31-16ab566a33cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(np.log10(metab_data.loc[meta_matched.query(\"VisitCode == 'V5'\").index, 'Phenylpyruvic acid']), meta_matched.query(\"VisitCode == 'V5'\")['median_mmNorm'])\n",
    "_ = plt.xlabel('Log Phenylpyruvic acid abundance')\n",
    "_ = plt.ylabel('median_mmNorm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "559d6dee-82ea-4fae-8b0b-218985848525",
   "metadata": {},
   "source": [
    "Plot ranks against each other since this is what spearman is actually using."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cb5d7cd1-8825-4414-b59e-4197fedd639f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ =sns.scatterplot(x='Phenylpyruvic acid', y='median_mmNorm', hue='VisitCode',\n",
    "                   data=pd.concat([metab_data.loc[meta_matched.index], meta_matched], axis=1).query(\"VisitCode == 'V5'\").rank())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1065224c-79e6-4d85-8d58-952e564594ae",
   "metadata": {},
   "source": [
    "### 1 year (V9) correlations\n",
    "\n",
    "Same correlations but with year 1 un-normalized data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d45b2557-fc9d-42a9-ac98-babe8ba4d779",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "111 3 0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>trans-4-Hydroxy-L-proline</th>\n",
       "      <td>-0.431247</td>\n",
       "      <td>0.007703</td>\n",
       "      <td>0.854997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>-0.338312</td>\n",
       "      <td>0.040553</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Threonine</th>\n",
       "      <td>-0.336890</td>\n",
       "      <td>0.041458</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Succinic acid</th>\n",
       "      <td>-0.292319</td>\n",
       "      <td>0.079148</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <td>-0.291304</td>\n",
       "      <td>0.080243</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Aspartic Acid</th>\n",
       "      <td>-0.283547</td>\n",
       "      <td>0.089008</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetyl D-galactosamine</th>\n",
       "      <td>-0.269710</td>\n",
       "      <td>0.106452</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Glutamine</th>\n",
       "      <td>-0.263395</td>\n",
       "      <td>0.115225</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <td>0.261514</td>\n",
       "      <td>0.117940</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetylneuraminic acid</th>\n",
       "      <td>-0.255571</td>\n",
       "      <td>0.126835</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate</th>\n",
       "      <td>0.252963</td>\n",
       "      <td>0.130892</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Proline</th>\n",
       "      <td>-0.247511</td>\n",
       "      <td>0.139685</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xanthosine</th>\n",
       "      <td>-0.238028</td>\n",
       "      <td>0.156000</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xanthurenic acid</th>\n",
       "      <td>-0.236605</td>\n",
       "      <td>0.158562</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allantoin</th>\n",
       "      <td>-0.232351</td>\n",
       "      <td>0.166403</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lactic acid</th>\n",
       "      <td>-0.231152</td>\n",
       "      <td>0.168664</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Carbamoyl-DL-aspartic acid</th>\n",
       "      <td>-0.223697</td>\n",
       "      <td>0.183210</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thymine</th>\n",
       "      <td>-0.215979</td>\n",
       "      <td>0.199186</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-hydroxyphenylacetate</th>\n",
       "      <td>-0.204125</td>\n",
       "      <td>0.225574</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orotic acid</th>\n",
       "      <td>-0.191323</td>\n",
       "      <td>0.256650</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate-d7</th>\n",
       "      <td>0.190849</td>\n",
       "      <td>0.257853</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetyl-alpha-D-glucosamine 1-phosphate</th>\n",
       "      <td>-0.187767</td>\n",
       "      <td>0.265763</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hydroxyphenyllactate</th>\n",
       "      <td>-0.182551</td>\n",
       "      <td>0.279510</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Glutamic acid</th>\n",
       "      <td>-0.178995</td>\n",
       "      <td>0.289143</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDCA</th>\n",
       "      <td>-0.177177</td>\n",
       "      <td>0.294148</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hippurate</th>\n",
       "      <td>-0.176387</td>\n",
       "      <td>0.296342</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oxamic acid</th>\n",
       "      <td>-0.174490</td>\n",
       "      <td>0.301649</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Tyrosine</th>\n",
       "      <td>-0.169749</td>\n",
       "      <td>0.315179</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-Methyl-2-oxovaleric acid</th>\n",
       "      <td>-0.167141</td>\n",
       "      <td>0.322780</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phenyl sulfate</th>\n",
       "      <td>-0.165718</td>\n",
       "      <td>0.326974</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xylitol</th>\n",
       "      <td>-0.163822</td>\n",
       "      <td>0.332619</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homocitrate</th>\n",
       "      <td>0.162912</td>\n",
       "      <td>0.335347</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Itaconic acid</th>\n",
       "      <td>-0.162162</td>\n",
       "      <td>0.337606</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kynurenic acid</th>\n",
       "      <td>-0.157421</td>\n",
       "      <td>0.352109</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyruvic acid</th>\n",
       "      <td>-0.154813</td>\n",
       "      <td>0.360244</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maleic acid</th>\n",
       "      <td>-0.148649</td>\n",
       "      <td>0.379915</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Citrulline</th>\n",
       "      <td>-0.145330</td>\n",
       "      <td>0.390764</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indole lactate</th>\n",
       "      <td>-0.144855</td>\n",
       "      <td>0.392328</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xanthine</th>\n",
       "      <td>-0.143907</td>\n",
       "      <td>0.395467</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glyceric acid</th>\n",
       "      <td>-0.143433</td>\n",
       "      <td>0.397043</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Tryptophan</th>\n",
       "      <td>-0.139640</td>\n",
       "      <td>0.409774</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-asparagine</th>\n",
       "      <td>-0.137743</td>\n",
       "      <td>0.416226</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha-D(+)Mannose 1-phosphate</th>\n",
       "      <td>-0.137032</td>\n",
       "      <td>0.418660</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-hydroxy-hippuric acid</th>\n",
       "      <td>-0.136795</td>\n",
       "      <td>0.419473</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Taurine</th>\n",
       "      <td>-0.134661</td>\n",
       "      <td>0.426831</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mevalonic acid</th>\n",
       "      <td>-0.128497</td>\n",
       "      <td>0.448487</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               rho   p_value     p_adj\n",
       "trans-4-Hydroxy-L-proline                -0.431247  0.007703  0.854997\n",
       "L-Serine                                 -0.338312  0.040553  0.970479\n",
       "L-Threonine                              -0.336890  0.041458  0.970479\n",
       "Succinic acid                            -0.292319  0.079148  0.970479\n",
       "Nicotinic acid                           -0.291304  0.080243  0.970479\n",
       "L-Aspartic Acid                          -0.283547  0.089008  0.970479\n",
       "N-Acetyl D-galactosamine                 -0.269710  0.106452  0.970479\n",
       "L-Glutamine                              -0.263395  0.115225  0.970479\n",
       "2-Deoxyuridine                            0.261514  0.117940  0.970479\n",
       "N-Acetylneuraminic acid                  -0.255571  0.126835  0.970479\n",
       "p-cresol sulfate                          0.252963  0.130892  0.970479\n",
       "L-Proline                                -0.247511  0.139685  0.970479\n",
       "Xanthosine                               -0.238028  0.156000  0.970479\n",
       "xanthurenic acid                         -0.236605  0.158562  0.970479\n",
       "Allantoin                                -0.232351  0.166403  0.970479\n",
       "Lactic acid                              -0.231152  0.168664  0.970479\n",
       "N-Carbamoyl-DL-aspartic acid             -0.223697  0.183210  0.970479\n",
       "Thymine                                  -0.215979  0.199186  0.970479\n",
       "4-hydroxyphenylacetate                   -0.204125  0.225574  0.970479\n",
       "Orotic acid                              -0.191323  0.256650  0.970479\n",
       "p-cresol sulfate-d7                       0.190849  0.257853  0.970479\n",
       "N-Acetyl-alpha-D-glucosamine 1-phosphate -0.187767  0.265763  0.970479\n",
       "hydroxyphenyllactate                     -0.182551  0.279510  0.970479\n",
       "L-Glutamic acid                          -0.178995  0.289143  0.970479\n",
       "GDCA                                     -0.177177  0.294148  0.970479\n",
       "hippurate                                -0.176387  0.296342  0.970479\n",
       "Oxamic acid                              -0.174490  0.301649  0.970479\n",
       "L-Tyrosine                               -0.169749  0.315179  0.970479\n",
       "4-Methyl-2-oxovaleric acid               -0.167141  0.322780  0.970479\n",
       "phenyl sulfate                           -0.165718  0.326974  0.970479\n",
       "Xylitol                                  -0.163822  0.332619  0.970479\n",
       "Homocitrate                               0.162912  0.335347  0.970479\n",
       "Itaconic acid                            -0.162162  0.337606  0.970479\n",
       "kynurenic acid                           -0.157421  0.352109  0.970479\n",
       "Pyruvic acid                             -0.154813  0.360244  0.970479\n",
       "Maleic acid                              -0.148649  0.379915  0.970479\n",
       "L-Citrulline                             -0.145330  0.390764  0.970479\n",
       "indole lactate                           -0.144855  0.392328  0.970479\n",
       "Xanthine                                 -0.143907  0.395467  0.970479\n",
       "Glyceric acid                            -0.143433  0.397043  0.970479\n",
       "L-Tryptophan                             -0.139640  0.409774  0.970479\n",
       "L-asparagine                             -0.137743  0.416226  0.970479\n",
       "alpha-D(+)Mannose 1-phosphate            -0.137032  0.418660  0.970479\n",
       "p-hydroxy-hippuric acid                  -0.136795  0.419473  0.970479\n",
       "Taurine                                  -0.134661  0.426831  0.970479\n",
       "Mevalonic acid                           -0.128497  0.448487  0.970479"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_v9 = meta_matched.query(\"VisitCode == 'V9'\")\n",
    "metab_data_v9 = metab_data.loc[meta_v9.index]\n",
    "# metab_data_v9 = metab_data_v9[(metab_data_v9 > 0).sum(axis=0) > metab_data_v9.shape[0]*.2]\n",
    "v9_correlations = metab_data_v9.apply(spearmanr, b=meta_v9['median_mmNorm']).transpose()\n",
    "v9_correlations.columns = ['rho', 'p_value']\n",
    "v9_correlations = v9_correlations.loc[~pd.isna(v9_correlations['p_value'])]\n",
    "v9_correlations['p_adj'] = p_adjust(v9_correlations['p_value'])\n",
    "v9_correlations = v9_correlations.sort_values('p_value')\n",
    "print(len(v9_correlations), len(v9_correlations.query('p_value < .05')), len(v9_correlations.query('p_adj < .05')))\n",
    "v9_correlations.head(46)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a03d5953-d901-4d7c-83a8-ce7cb9894b88",
   "metadata": {},
   "source": [
    "45 compounds have significant adjusted p-values. 62 significant raw p-values. So about half of things are significant. Is this an artifact or does it mean that base metabolism is associated with titer response?\n",
    "\n",
    "Tryptophan D5 is in this list. It is a control compound so we probably shouldn't trust this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0348866c-9d90-42d7-a36c-2cff5ee4ee48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L-tryptophan-D5</th>\n",
       "      <td>0.116406</td>\n",
       "      <td>0.492647</td>\n",
       "      <td>0.970479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      rho   p_value     p_adj\n",
       "L-tryptophan-D5  0.116406  0.492647  0.970479"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v9_correlations.loc[[i for i in v9_correlations.index if 'D5' in i]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a98af5b1-0b99-4f9d-8f65-6436b95dd424",
   "metadata": {},
   "source": [
    "#### 5-HIAA\n",
    "\n",
    "At 1 year correlation between 5-HIAA and median titer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6bab5203-523b-414c-803d-c7ee65fd4844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.scatterplot(x='5-HIAA', y='median_mmNorm', hue='VisitCode',\n",
    "                    data=pd.concat([metab_data.loc[meta_matched.index], meta_matched], axis=1).query(\"VisitCode == 'V9'\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "639a1831-b75c-4d88-8d82-74b7cc327365",
   "metadata": {},
   "source": [
    "Scatter plot showing correlation between ranks and titer as that is what is actually measured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f55debcf-34b7-4ec9-a695-8f6fb6cf3136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.scatterplot(x='5-HIAA', y='median_mmNorm', hue='VisitCode',\n",
    "                    data=pd.concat([metab_data.loc[meta_matched.index], meta_matched], axis=1).query(\"VisitCode == 'V9'\").rank())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589cb497-ea1b-4665-8ac8-aec2dbeacb9a",
   "metadata": {},
   "source": [
    "Scatterplot of 5-HIAA un-normalized vs median titer with points colored by visit code. V5 is 2 months and V9 is one year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "85030b23-c193-4225-95f0-cfd0edf5dfb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.scatterplot(x='5-HIAA', y='median_mmNorm', hue='VisitCode',\n",
    "                    data=pd.concat([metab_data.loc[meta_matched.index], meta_matched], axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cab0143b-d953-4817-8555-5e087df7da13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(np.log10(metab_data.loc[meta_matched.query(\"VisitCode == 'V9'\").index, '5-HIAA']), meta_matched.query(\"VisitCode == 'V9'\")['median_mmNorm'])\n",
    "_ = plt.xlabel('Log 5-HIAA abundance')\n",
    "_ = plt.ylabel('median_mmNorm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8519510e-0f09-4d44-83d7-3ed498742e86",
   "metadata": {},
   "source": [
    "## Correlation with relative abundance data\n",
    "\n",
    "Try normalizing metabolomics to relative abundance before doing correlations. Want to see if this will reduce number of correlated metabolites at 1 year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "22366cd0-afb9-49d0-8ecc-1f9af6dcc0e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Uridine</th>\n",
       "      <td>-0.492063</td>\n",
       "      <td>0.007820</td>\n",
       "      <td>0.706924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hydroxyphenyllactate</th>\n",
       "      <td>-0.445539</td>\n",
       "      <td>0.017498</td>\n",
       "      <td>0.706924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>0.440066</td>\n",
       "      <td>0.019106</td>\n",
       "      <td>0.706924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>0.416530</td>\n",
       "      <td>0.027462</td>\n",
       "      <td>0.732144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <td>-0.398467</td>\n",
       "      <td>0.035708</td>\n",
       "      <td>0.732144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate</th>\n",
       "      <td>0.384784</td>\n",
       "      <td>0.043191</td>\n",
       "      <td>0.732144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Aspartic Acid</th>\n",
       "      <td>-0.379858</td>\n",
       "      <td>0.046171</td>\n",
       "      <td>0.732144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Carbamoyl-DL-aspartic acid</th>\n",
       "      <td>-0.344280</td>\n",
       "      <td>0.072813</td>\n",
       "      <td>0.885314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <td>-0.343733</td>\n",
       "      <td>0.073300</td>\n",
       "      <td>0.885314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>0.308155</td>\n",
       "      <td>0.110631</td>\n",
       "      <td>0.885314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thymine</th>\n",
       "      <td>-0.304871</td>\n",
       "      <td>0.114681</td>\n",
       "      <td>0.885314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melibiose</th>\n",
       "      <td>0.299398</td>\n",
       "      <td>0.121674</td>\n",
       "      <td>0.885314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.288998</td>\n",
       "      <td>0.135817</td>\n",
       "      <td>0.885314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phenaceturic acid</th>\n",
       "      <td>0.269294</td>\n",
       "      <td>0.165821</td>\n",
       "      <td>0.885314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indoxyl sulfate-13C6</th>\n",
       "      <td>-0.256705</td>\n",
       "      <td>0.187286</td>\n",
       "      <td>0.885314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   rho   p_value     p_adj\n",
       "Uridine                      -0.492063  0.007820  0.706924\n",
       "hydroxyphenyllactate         -0.445539  0.017498  0.706924\n",
       "Guanosine                     0.440066  0.019106  0.706924\n",
       "5-HIAA                        0.416530  0.027462  0.732144\n",
       "Nicotinic acid               -0.398467  0.035708  0.732144\n",
       "p-cresol sulfate              0.384784  0.043191  0.732144\n",
       "L-Aspartic Acid              -0.379858  0.046171  0.732144\n",
       "N-Carbamoyl-DL-aspartic acid -0.344280  0.072813  0.885314\n",
       "2-Deoxyuridine               -0.343733  0.073300  0.885314\n",
       "Inosine                       0.308155  0.110631  0.885314\n",
       "Thymine                      -0.304871  0.114681  0.885314\n",
       "Melibiose                     0.299398  0.121674  0.885314\n",
       "serotonin                     0.288998  0.135817  0.885314\n",
       "Phenaceturic acid             0.269294  0.165821  0.885314\n",
       "indoxyl sulfate-13C6         -0.256705  0.187286  0.885314"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_rel_v5 = metab_data_rel.loc[meta_v5.index]\n",
    "# metab_data_v5 = metab_data_v5[(metab_data_v5 > 0).sum(axis=0) > metab_data_v5.shape[0]*.2]\n",
    "v5_correlations_rel = metab_data_rel_v5.apply(spearmanr, b=meta_v5['median_mmNorm']).transpose()\n",
    "v5_correlations_rel.columns = ['rho', 'p_value']\n",
    "v5_correlations_rel['p_adj'] = p_adjust(v5_correlations_rel['p_value'])\n",
    "v5_correlations_rel = v5_correlations_rel.sort_values('p_value')\n",
    "v5_correlations_rel.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec5d511d-53b8-4d44-a6f4-311d5a5b74e1",
   "metadata": {},
   "source": [
    "Phenylpyruvic acide remains very significant at month 2 with no other significant results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d25f672d-e058-4256-bd1d-7cca71ec0534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>trans-4-Hydroxy-L-proline</th>\n",
       "      <td>-0.359649</td>\n",
       "      <td>0.028793</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>-0.334519</td>\n",
       "      <td>0.043003</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <td>0.316501</td>\n",
       "      <td>0.056326</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Aspartic Acid</th>\n",
       "      <td>-0.299905</td>\n",
       "      <td>0.071324</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <td>-0.296823</td>\n",
       "      <td>0.074426</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Threonine</th>\n",
       "      <td>-0.293030</td>\n",
       "      <td>0.078387</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate</th>\n",
       "      <td>0.285443</td>\n",
       "      <td>0.086800</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetylneuraminic acid</th>\n",
       "      <td>-0.279991</td>\n",
       "      <td>0.093265</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetyl D-galactosamine</th>\n",
       "      <td>-0.277857</td>\n",
       "      <td>0.095893</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Glutamine</th>\n",
       "      <td>-0.261498</td>\n",
       "      <td>0.117963</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetyl-alpha-D-glucosamine 1-phosphate</th>\n",
       "      <td>-0.249407</td>\n",
       "      <td>0.136578</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xanthurenic acid</th>\n",
       "      <td>-0.248696</td>\n",
       "      <td>0.137737</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xanthosine</th>\n",
       "      <td>-0.247748</td>\n",
       "      <td>0.139294</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Proline</th>\n",
       "      <td>-0.215505</td>\n",
       "      <td>0.200198</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glyceric acid</th>\n",
       "      <td>-0.197724</td>\n",
       "      <td>0.240774</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-hydroxyphenylacetate</th>\n",
       "      <td>-0.196302</td>\n",
       "      <td>0.244244</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Carbamoyl-DL-aspartic acid</th>\n",
       "      <td>-0.191560</td>\n",
       "      <td>0.256050</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Phenylalanine</th>\n",
       "      <td>0.190612</td>\n",
       "      <td>0.258455</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDCA</th>\n",
       "      <td>-0.187684</td>\n",
       "      <td>0.265978</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orotic acid</th>\n",
       "      <td>-0.186344</td>\n",
       "      <td>0.269467</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homocitrate</th>\n",
       "      <td>0.181840</td>\n",
       "      <td>0.281420</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Allantoin</th>\n",
       "      <td>-0.178995</td>\n",
       "      <td>0.289143</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate-d7</th>\n",
       "      <td>0.177098</td>\n",
       "      <td>0.294367</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indoleacrylicacid</th>\n",
       "      <td>0.175676</td>\n",
       "      <td>0.298325</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thymine</th>\n",
       "      <td>-0.165007</td>\n",
       "      <td>0.329084</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-3-Dihydroxyisovalerate</th>\n",
       "      <td>0.163348</td>\n",
       "      <td>0.334039</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHDCA or GUDCA</th>\n",
       "      <td>0.161925</td>\n",
       "      <td>0.338323</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phenyl sulfate</th>\n",
       "      <td>-0.161925</td>\n",
       "      <td>0.338323</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hydroxyphenyllactate</th>\n",
       "      <td>-0.161688</td>\n",
       "      <td>0.339040</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xanthine</th>\n",
       "      <td>-0.151257</td>\n",
       "      <td>0.371517</td>\n",
       "      <td>0.940846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               rho   p_value     p_adj\n",
       "trans-4-Hydroxy-L-proline                -0.359649  0.028793  0.940846\n",
       "L-Serine                                 -0.334519  0.043003  0.940846\n",
       "2-Deoxyuridine                            0.316501  0.056326  0.940846\n",
       "L-Aspartic Acid                          -0.299905  0.071324  0.940846\n",
       "Nicotinic acid                           -0.296823  0.074426  0.940846\n",
       "L-Threonine                              -0.293030  0.078387  0.940846\n",
       "p-cresol sulfate                          0.285443  0.086800  0.940846\n",
       "N-Acetylneuraminic acid                  -0.279991  0.093265  0.940846\n",
       "N-Acetyl D-galactosamine                 -0.277857  0.095893  0.940846\n",
       "L-Glutamine                              -0.261498  0.117963  0.940846\n",
       "N-Acetyl-alpha-D-glucosamine 1-phosphate -0.249407  0.136578  0.940846\n",
       "xanthurenic acid                         -0.248696  0.137737  0.940846\n",
       "Xanthosine                               -0.247748  0.139294  0.940846\n",
       "L-Proline                                -0.215505  0.200198  0.940846\n",
       "Glyceric acid                            -0.197724  0.240774  0.940846\n",
       "4-hydroxyphenylacetate                   -0.196302  0.244244  0.940846\n",
       "N-Carbamoyl-DL-aspartic acid             -0.191560  0.256050  0.940846\n",
       "L-Phenylalanine                           0.190612  0.258455  0.940846\n",
       "GDCA                                     -0.187684  0.265978  0.940846\n",
       "Orotic acid                              -0.186344  0.269467  0.940846\n",
       "Homocitrate                               0.181840  0.281420  0.940846\n",
       "Allantoin                                -0.178995  0.289143  0.940846\n",
       "p-cresol sulfate-d7                       0.177098  0.294367  0.940846\n",
       "indoleacrylicacid                         0.175676  0.298325  0.940846\n",
       "Thymine                                  -0.165007  0.329084  0.940846\n",
       "2-3-Dihydroxyisovalerate                  0.163348  0.334039  0.940846\n",
       "GHDCA or GUDCA                            0.161925  0.338323  0.940846\n",
       "phenyl sulfate                           -0.161925  0.338323  0.940846\n",
       "hydroxyphenyllactate                     -0.161688  0.339040  0.940846\n",
       "Xanthine                                 -0.151257  0.371517  0.940846"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_rel_v9 = metab_data_rel.loc[meta_v9.index]\n",
    "# metab_data_v9 = metab_data_v9[(metab_data_v9 > 0).sum(axis=0) > metab_data_v9.shape[0]*.2]\n",
    "v9_correlations_rel = metab_data_rel_v9.apply(spearmanr, b=meta_v9['median_mmNorm']).transpose()\n",
    "v9_correlations_rel.columns = ['rho', 'p_value']\n",
    "v9_correlations_rel = v9_correlations_rel.loc[~pd.isna(v9_correlations_rel['p_value'])]\n",
    "v9_correlations_rel['p_adj'] = p_adjust(v9_correlations_rel['p_value'])\n",
    "v9_correlations_rel = v9_correlations_rel.sort_values('p_value')\n",
    "v9_correlations_rel.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0195daf9-2633-443f-8165-36a232835ae4",
   "metadata": {},
   "source": [
    "30 things significant at 12 months with relative abundance normalization. Down from 45 (out of 52)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6441be6e-ee30-43d2-9170-e1711910d75d",
   "metadata": {},
   "source": [
    "## Correlation with CLR'd data\n",
    "\n",
    "Now let's try with CLR adjusted data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "53bb7f55-5bff-40af-986d-6a855a689d79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>0.415435</td>\n",
       "      <td>0.027913</td>\n",
       "      <td>0.994666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>0.406130</td>\n",
       "      <td>0.031996</td>\n",
       "      <td>0.994666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hydroxyphenyllactate</th>\n",
       "      <td>-0.384236</td>\n",
       "      <td>0.043514</td>\n",
       "      <td>0.994666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate</th>\n",
       "      <td>0.368911</td>\n",
       "      <td>0.053377</td>\n",
       "      <td>0.994666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <td>-0.357417</td>\n",
       "      <td>0.061865</td>\n",
       "      <td>0.994666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melibiose</th>\n",
       "      <td>0.349754</td>\n",
       "      <td>0.068083</td>\n",
       "      <td>0.994666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Carbamoyl-DL-aspartic acid</th>\n",
       "      <td>-0.306513</td>\n",
       "      <td>0.112642</td>\n",
       "      <td>0.994666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.305419</td>\n",
       "      <td>0.113999</td>\n",
       "      <td>0.994666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>0.301040</td>\n",
       "      <td>0.119544</td>\n",
       "      <td>0.994666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Aspartic Acid</th>\n",
       "      <td>-0.290640</td>\n",
       "      <td>0.133508</td>\n",
       "      <td>0.994666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phenaceturic acid</th>\n",
       "      <td>0.274767</td>\n",
       "      <td>0.157054</td>\n",
       "      <td>0.994666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uridine</th>\n",
       "      <td>-0.274767</td>\n",
       "      <td>0.157054</td>\n",
       "      <td>0.994666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   rho   p_value     p_adj\n",
       "Guanosine                     0.415435  0.027913  0.994666\n",
       "5-HIAA                        0.406130  0.031996  0.994666\n",
       "hydroxyphenyllactate         -0.384236  0.043514  0.994666\n",
       "p-cresol sulfate              0.368911  0.053377  0.994666\n",
       "Nicotinic acid               -0.357417  0.061865  0.994666\n",
       "Melibiose                     0.349754  0.068083  0.994666\n",
       "N-Carbamoyl-DL-aspartic acid -0.306513  0.112642  0.994666\n",
       "serotonin                     0.305419  0.113999  0.994666\n",
       "Inosine                       0.301040  0.119544  0.994666\n",
       "L-Aspartic Acid              -0.290640  0.133508  0.994666\n",
       "Phenaceturic acid             0.274767  0.157054  0.994666\n",
       "Uridine                      -0.274767  0.157054  0.994666"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_clr_v5 = metab_data_clr.loc[meta_v5.index]\n",
    "# metab_data_v5 = metab_data_v5[(metab_data_v5 > 0).sum(axis=0) > metab_data_v5.shape[0]*.2]\n",
    "v5_correlations_clr = metab_data_clr_v5.apply(spearmanr, b=meta_v5['median_mmNorm']).transpose()\n",
    "v5_correlations_clr.columns = ['rho', 'p_value']\n",
    "v5_correlations_clr['p_adj'] = p_adjust(v5_correlations_clr['p_value'])\n",
    "v5_correlations_clr = v5_correlations_clr.sort_values('p_value')\n",
    "v5_correlations_clr.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b6c1c0-6ae3-41f0-bc16-59ea5fb3f136",
   "metadata": {},
   "source": [
    "Hooray! Still Phenylpyruvic acid and nothing else."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d516ce45-39ec-4b55-af84-a8050c0689bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <td>0.362494</td>\n",
       "      <td>0.027462</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate</th>\n",
       "      <td>0.330014</td>\n",
       "      <td>0.046067</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>-0.322902</td>\n",
       "      <td>0.051260</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <td>-0.298720</td>\n",
       "      <td>0.072505</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Phenylalanine</th>\n",
       "      <td>0.292793</td>\n",
       "      <td>0.078640</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xanthosine</th>\n",
       "      <td>-0.291607</td>\n",
       "      <td>0.079914</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Threonine</th>\n",
       "      <td>-0.285206</td>\n",
       "      <td>0.087074</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Aspartic Acid</th>\n",
       "      <td>-0.256757</td>\n",
       "      <td>0.125022</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetyl D-galactosamine</th>\n",
       "      <td>-0.254386</td>\n",
       "      <td>0.128667</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetylneuraminic acid</th>\n",
       "      <td>-0.252489</td>\n",
       "      <td>0.131640</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trans-4-Hydroxy-L-proline</th>\n",
       "      <td>-0.252015</td>\n",
       "      <td>0.132391</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creatine</th>\n",
       "      <td>0.221669</td>\n",
       "      <td>0.187318</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate-d7</th>\n",
       "      <td>0.221432</td>\n",
       "      <td>0.187802</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Proline</th>\n",
       "      <td>-0.221432</td>\n",
       "      <td>0.187802</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-phenyllactic acid</th>\n",
       "      <td>0.217165</td>\n",
       "      <td>0.196671</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Arabinose</th>\n",
       "      <td>0.212423</td>\n",
       "      <td>0.206865</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Glutamine</th>\n",
       "      <td>-0.209104</td>\n",
       "      <td>0.214215</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D-Xylose</th>\n",
       "      <td>0.208867</td>\n",
       "      <td>0.214747</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHDCA or GUDCA</th>\n",
       "      <td>0.203177</td>\n",
       "      <td>0.227784</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indoxyl sulfate-13C6</th>\n",
       "      <td>0.201754</td>\n",
       "      <td>0.231126</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homocitrate</th>\n",
       "      <td>0.196064</td>\n",
       "      <td>0.244825</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Methionine</th>\n",
       "      <td>0.194168</td>\n",
       "      <td>0.249510</td>\n",
       "      <td>0.951705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                rho   p_value     p_adj\n",
       "2-Deoxyuridine             0.362494  0.027462  0.951705\n",
       "p-cresol sulfate           0.330014  0.046067  0.951705\n",
       "L-Serine                  -0.322902  0.051260  0.951705\n",
       "Nicotinic acid            -0.298720  0.072505  0.951705\n",
       "L-Phenylalanine            0.292793  0.078640  0.951705\n",
       "Xanthosine                -0.291607  0.079914  0.951705\n",
       "L-Threonine               -0.285206  0.087074  0.951705\n",
       "L-Aspartic Acid           -0.256757  0.125022  0.951705\n",
       "N-Acetyl D-galactosamine  -0.254386  0.128667  0.951705\n",
       "N-Acetylneuraminic acid   -0.252489  0.131640  0.951705\n",
       "trans-4-Hydroxy-L-proline -0.252015  0.132391  0.951705\n",
       "Creatine                   0.221669  0.187318  0.951705\n",
       "p-cresol sulfate-d7        0.221432  0.187802  0.951705\n",
       "L-Proline                 -0.221432  0.187802  0.951705\n",
       "3-phenyllactic acid        0.217165  0.196671  0.951705\n",
       "L-Arabinose                0.212423  0.206865  0.951705\n",
       "L-Glutamine               -0.209104  0.214215  0.951705\n",
       "D-Xylose                   0.208867  0.214747  0.951705\n",
       "GHDCA or GUDCA             0.203177  0.227784  0.951705\n",
       "indoxyl sulfate-13C6       0.201754  0.231126  0.951705\n",
       "Homocitrate                0.196064  0.244825  0.951705\n",
       "L-Methionine               0.194168  0.249510  0.951705"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_clr_v9 = metab_data_clr.loc[meta_v9.index]\n",
    "# metab_data_v9 = metab_data_v9[(metab_data_v9 > 0).sum(axis=0) > metab_data_v9.shape[0]*.2]\n",
    "v9_correlations_clr = metab_data_clr_v9.apply(spearmanr, b=meta_v9['median_mmNorm']).transpose()\n",
    "v9_correlations_clr.columns = ['rho', 'p_value']\n",
    "v9_correlations_clr = v9_correlations_clr.loc[~pd.isna(v9_correlations_clr['p_value'])]\n",
    "v9_correlations_clr['p_adj'] = p_adjust(v9_correlations_clr['p_value'])\n",
    "v9_correlations_clr = v9_correlations_clr.sort_values('p_value')\n",
    "v9_correlations_clr.head(22)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28b30b3e-9c54-41db-a374-34c7291db186",
   "metadata": {},
   "source": [
    "Down to 21 significant things at 1 year with CLR so more \"advanced\" normalizations keep getting rid of significant results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0c0a95-4107-4ed7-a88d-bcab473c7b4e",
   "metadata": {},
   "source": [
    "## Compare normalizations\n",
    "\n",
    "To better understand what the normalization are shifting we compare between normalizations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9fb334e-e935-4730-b3af-2f7c2922a1c4",
   "metadata": {},
   "source": [
    "### V5\n",
    "\n",
    "These are scatterplots of p-values between normalization at 2 months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9289c443-8c31-4820-a89e-d22499464b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.9423136073051837, pvalue=1.2573233674349883e-53)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(-1*np.log10(v5_correlations['p_value']), -1*np.log10(v5_correlations_rel.loc[v5_correlations.index, 'p_value']))\n",
    "_ = plt.xlabel('-1*log10 Un-normalized p-values')\n",
    "_ = plt.ylabel('-1*log10 Relative abundanace p-values')\n",
    "pearsonr(-1*np.log10(v5_correlations['p_value']), -1*np.log10(v5_correlations_rel.loc[v5_correlations.index, 'p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e50d939-bf00-41dc-8481-35c6ad71af89",
   "metadata": {},
   "source": [
    "Un-normalized and relative abundance are highly correlated but a bit of variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6e7c1ab8-fa75-455b-9072-22e394563882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.9149979088419075, pvalue=8.95652368336839e-45)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(-1*np.log10(v5_correlations['p_value']), -1*np.log10(v5_correlations_clr.loc[v5_correlations.index, 'p_value']))\n",
    "_ = plt.xlabel('-1*log10 Un-normalized p-values')\n",
    "_ = plt.ylabel('-1*log10 CLR p-values')\n",
    "pearsonr(-1*np.log10(v5_correlations['p_value']), -1*np.log10(v5_correlations_clr.loc[v5_correlations.index, 'p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2499820f-6d45-4767-9a33-e79a3c178950",
   "metadata": {},
   "source": [
    "Much more spread with un-normalized vs CLR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "82146a1f-0656-41c7-85cb-dd963d54ae5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.8595175132598343, pvalue=1.4939194319193217e-33)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(-1*np.log10(v5_correlations_rel['p_value']), -1*np.log10(v5_correlations_clr.loc[v5_correlations_rel.index, 'p_value']))\n",
    "_ = plt.xlabel('-1*log10 Relative abundanace p-values')\n",
    "_ = plt.ylabel('-1*log10 CLR p-values')\n",
    "pearsonr(-1*np.log10(v5_correlations_rel['p_value']), -1*np.log10(v5_correlations_clr.loc[v5_correlations_rel.index, 'p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aabee09e-4219-4377-8c10-ba96d8f42f75",
   "metadata": {},
   "source": [
    "Less correlation between relative abundance and CLR but still pretty good.\n",
    "\n",
    "Across all normalizations phenylpyruvic acid is super significant. You can definitely see that relative abundance and CLR shift the p-value distributions but they are much more strongly correlated with each other than the raw data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42464bf1-6ac3-4b3c-b018-ebe8a09ba7c1",
   "metadata": {},
   "source": [
    "### V9\n",
    "\n",
    "Looking at the 1 year results.\n",
    "\n",
    "To do:\n",
    "* Try plotting L-tryptophan-D5, it is a standard, potentially use as a normalization factor (?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a41bc7d4-7f61-4769-84e3-55e7a0331e91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(v9_correlations.query('p_adj < .05').index) & set(v9_correlations_rel.query('p_adj < .05').index) & set(v9_correlations_clr.query('p_adj < .05').index)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed0ddd3-5a12-490e-a6c6-ca9457712dcd",
   "metadata": {},
   "source": [
    "These are the 10 compounds that are significant in all 3 methods. The L-tryptophan-D5 is a spike-in. There are multiple spike-ins used and these is the only one that comes us as significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "758c61c7-0f88-4e0c-b2c3-1c80008f4959",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shaffmic/miniconda3/envs/imc/lib/python3.9/site-packages/matplotlib_venn/_venn3.py:47: UserWarning: All circles have zero area\n",
      "  warnings.warn(\"All circles have zero area\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = venn3([set(v9_correlations.query('p_adj < .05').index), set(v9_correlations_rel.query('p_adj < .05').index), set(v9_correlations_clr.query('p_adj < .05').index)], set_labels=('un-normalized', 'relative abundance', 'CLR'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1226f73a-dadb-4777-9c08-71ae69dcbba7",
   "metadata": {},
   "source": [
    "The venn diagram shows the 10 that are shared by all. 20 are only in the normalized data. Relative abundance doesn't add anything unique and CLR adds two unique results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2b7a0355-11d7-479f-b19d-cbbcb6772383",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.8271895288562003, pvalue=4.7600578110268337e-29)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(-1*np.log10(v9_correlations['p_value']), -1*np.log10(v9_correlations_rel.loc[v9_correlations.index, 'p_value']))\n",
    "_ = plt.xlabel('-1*log10 Un-normalized p-values')\n",
    "_ = plt.ylabel('-1*log10 Relative abundanace p-values')\n",
    "pearsonr(-1*np.log10(v9_correlations['p_value']), -1*np.log10(v9_correlations_rel.loc[v9_correlations.index, 'p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d72d019d-d105-4ac3-a590-624fb9072398",
   "metadata": {},
   "source": [
    "Much worse correlation between normalization strategies at one year time point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3aff190e-1a00-40ce-97f4-3f1f6e7f6da0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.4946819535624442, pvalue=3.417823844785238e-08)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(-1*np.log10(v9_correlations['p_value']), -1*np.log10(v9_correlations_clr.loc[v9_correlations.index, 'p_value']))\n",
    "_ = plt.xlabel('-1*log10 Un-normalized p-values')\n",
    "_ = plt.ylabel('-1*log10 CLR p-values')\n",
    "pearsonr(-1*np.log10(v9_correlations['p_value']), -1*np.log10(v9_correlations_clr.loc[v9_correlations.index, 'p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c62d699f-1967-469e-86c7-27a860056939",
   "metadata": {},
   "source": [
    "Much worse correlation between normalization strategies at one year time point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8682195e-7b4e-4d05-9757-89a1a1ce8445",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PearsonRResult(statistic=0.7948366038030101, pvalue=2.1552737495252317e-25)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(-1*np.log10(v9_correlations_rel['p_value']), -1*np.log10(v9_correlations_clr.loc[v9_correlations_rel.index, 'p_value']))\n",
    "_ = plt.xlabel('-1*log10 relative abundance p-values')\n",
    "_ = plt.ylabel('-1*log10 CLR p-values')\n",
    "pearsonr(-1*np.log10(v9_correlations_rel['p_value']), -1*np.log10(v9_correlations_clr.loc[v9_correlations_rel.index, 'p_value']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "554c961c-aae2-4f49-93a7-a56b6bc165d1",
   "metadata": {},
   "source": [
    "Much worse correlation between normalization strategies at one year time point."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3906404d-c3ac-4003-9d69-1b24c07205e9",
   "metadata": {},
   "source": [
    "## Plot tryptophan vs titers\n",
    "\n",
    "This is the spike-in which was found to be significant at year 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "21647c37-a082-453c-8973-c4c81036cb93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(metab_data_v9.loc[meta_v9.index, 'L-tryptophan-D5'], meta_v9['median_mmNorm'])\n",
    "_ = plt.xlabel('L-tryptophan-D5 abundance')\n",
    "_ = plt.ylabel('Median normalized titer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b1ff4794-5b7f-4902-bcf5-1ad34e41f01e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(metab_data_rel_v9.loc[meta_v9.index, 'L-tryptophan-D5'], meta_v9['median_mmNorm'])\n",
    "_ = plt.xlabel('L-tryptophan-D5 relative abundance')\n",
    "_ = plt.ylabel('Median normalized titer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "683c0dc5-ba35-4772-97a4-3d095e746a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(metab_data_clr_v9.loc[meta_v9.index, 'L-tryptophan-D5'], meta_v9['median_mmNorm'])\n",
    "_ = plt.xlabel('L-tryptophan-D5 CLR abundance')\n",
    "_ = plt.ylabel('Median normalized titer')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2244404d-3a89-4024-b67d-f4e48c1e892a",
   "metadata": {},
   "source": [
    "It looks pretty correlated no matter what normalization you use. This make me hesitant to use the 1 year time point metabolomics data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c69a87e-e51f-4eaf-92a9-c1d9c06f53e8",
   "metadata": {},
   "source": [
    "## Correlate un-normalized with separate median titers\n",
    "\n",
    "Now that we have looked at the combined median titers with both time points we will split into the DTAPHib and PCV medians separately."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "326cc2ad-9a03-4603-9ba2-07112b10ab39",
   "metadata": {},
   "source": [
    "### 2 month DTap hib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "be5156a8-fbff-4645-a5af-a0b489c45c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L-asparagine</th>\n",
       "      <td>0.476738</td>\n",
       "      <td>0.010320</td>\n",
       "      <td>0.603381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Proline</th>\n",
       "      <td>0.461412</td>\n",
       "      <td>0.013455</td>\n",
       "      <td>0.603381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-hydroxy-hippuric acid</th>\n",
       "      <td>0.448672</td>\n",
       "      <td>0.016630</td>\n",
       "      <td>0.603381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Leucine</th>\n",
       "      <td>0.431856</td>\n",
       "      <td>0.021743</td>\n",
       "      <td>0.603381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hippurate</th>\n",
       "      <td>0.388615</td>\n",
       "      <td>0.040980</td>\n",
       "      <td>0.909767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              rho   p_value     p_adj\n",
       "L-asparagine             0.476738  0.010320  0.603381\n",
       "L-Proline                0.461412  0.013455  0.603381\n",
       "p-hydroxy-hippuric acid  0.448672  0.016630  0.603381\n",
       "L-Leucine                0.431856  0.021743  0.603381\n",
       "hippurate                0.388615  0.040980  0.909767"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v5_DTAPHib_correlations = metab_data_v5.apply(spearmanr, b=meta_v5['median_mmNorm_DTAPHib']).transpose()\n",
    "v5_DTAPHib_correlations.columns = ['rho', 'p_value']\n",
    "v5_DTAPHib_correlations['p_adj'] = p_adjust(v5_DTAPHib_correlations['p_value'])\n",
    "v5_DTAPHib_correlations = v5_DTAPHib_correlations.sort_values('p_value')\n",
    "v5_DTAPHib_correlations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1ed8ca3-c27c-4738-af6f-04b84dcfe111",
   "metadata": {},
   "source": [
    "Three things are significant after FDR correction when correlating 2 month titers with DTAPHib titers. Phenylpyruvic acid still and additionally pyruvic acid and 1-Methyl-1-butanol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "552d8ee0-4b06-4521-a2ac-72ccd1d38a1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metab_data_v5_w_titer = metab_data_v5.copy()\n",
    "metab_data_v5_w_titer['median_mmNorm_DTAPHib'] = meta_v5.loc[metab_data_v5_w_titer.index, 'median_mmNorm_DTAPHib']\n",
    "_ = sns.scatterplot(x='Pyruvic acid', y='median_mmNorm_DTAPHib', data=metab_data_v5_w_titer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cca1d702-fca7-4e49-895b-d80b00aeb1e0",
   "metadata": {},
   "source": [
    "Very similar curve to what we saw with phenylpyruvic acid with the median of all titers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bf0af138-fc0c-4ee2-ac8d-6935fb8b7fc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.scatterplot(x='Phenylpyruvic acid', y='median_mmNorm_DTAPHib', data=metab_data_v5_w_titer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cde25d6-76ee-42dd-96f8-c997336b4000",
   "metadata": {},
   "source": [
    "Again very similar to the other significant curves. This one might be a bit more extreme/scattered. If you have < 2000 Phenylpyruvic acid acid then you are very likely to have high titers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09c2c9d6-1444-46b7-bfa1-5940d64e30ff",
   "metadata": {},
   "source": [
    "### 2 month PCV\n",
    "\n",
    "Same idea as above but with the PCV titers instead of the DTapHib titers. There are two babies who have a median titer but do not have a median PCV titer because of a lack of measured PCV titers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3f75fee5-8a63-42b0-b0ed-aacbbc22d3f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BabyN</th>\n",
       "      <th>VisitCode</th>\n",
       "      <th>VisitDate</th>\n",
       "      <th>PT</th>\n",
       "      <th>Dip</th>\n",
       "      <th>FHA</th>\n",
       "      <th>PRN</th>\n",
       "      <th>TET</th>\n",
       "      <th>PRP (Hib)</th>\n",
       "      <th>PCV ST1</th>\n",
       "      <th>...</th>\n",
       "      <th>median_mmNorm</th>\n",
       "      <th>median_mmNorm_DTAPHib</th>\n",
       "      <th>median_mmNorm_PCV</th>\n",
       "      <th>PT_protected</th>\n",
       "      <th>Dip_protected</th>\n",
       "      <th>FHA_protected</th>\n",
       "      <th>PRN_protected</th>\n",
       "      <th>TET_protected</th>\n",
       "      <th>PRP (Hib)_protected</th>\n",
       "      <th>VR_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P251_V9_09092019</th>\n",
       "      <td>251</td>\n",
       "      <td>V9</td>\n",
       "      <td>09092019</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.17</td>\n",
       "      <td>7.35</td>\n",
       "      <td>26.071367</td>\n",
       "      <td>...</td>\n",
       "      <td>0.045279</td>\n",
       "      <td>0.013593</td>\n",
       "      <td>0.113246</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P261_V12_10222020</th>\n",
       "      <td>261</td>\n",
       "      <td>V12</td>\n",
       "      <td>10222020</td>\n",
       "      <td>21.0</td>\n",
       "      <td>1.03</td>\n",
       "      <td>8.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.05</td>\n",
       "      <td>31.157284</td>\n",
       "      <td>...</td>\n",
       "      <td>0.03523</td>\n",
       "      <td>0.102296</td>\n",
       "      <td>0.030523</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P226_V12_09302020</th>\n",
       "      <td>226</td>\n",
       "      <td>V12</td>\n",
       "      <td>09302020</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.68</td>\n",
       "      <td>20.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3.26</td>\n",
       "      <td>3.57</td>\n",
       "      <td>23.329774</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082202</td>\n",
       "      <td>0.097174</td>\n",
       "      <td>0.057712</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P210_V5_05062018</th>\n",
       "      <td>210</td>\n",
       "      <td>V5</td>\n",
       "      <td>05062018</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.66</td>\n",
       "      <td>23.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2.58</td>\n",
       "      <td>156.531647</td>\n",
       "      <td>...</td>\n",
       "      <td>0.083593</td>\n",
       "      <td>0.115757</td>\n",
       "      <td>0.063515</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P230_V9_07102019</th>\n",
       "      <td>230</td>\n",
       "      <td>V9</td>\n",
       "      <td>07102019</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.3</td>\n",
       "      <td>81.383885</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048516</td>\n",
       "      <td>0.02296</td>\n",
       "      <td>0.088023</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NVR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   BabyN VisitCode VisitDate    PT   Dip   FHA    PRN   TET  \\\n",
       "P251_V9_09092019     251        V9  09092019   5.0  0.22   1.5    2.5  0.17   \n",
       "P261_V12_10222020    261       V12  10222020  21.0  1.03   8.0  125.0  0.98   \n",
       "P226_V12_09302020    226       V12  09302020  16.0  0.68  20.0   32.0  3.26   \n",
       "P210_V5_05062018     210        V5  05062018  19.0  0.66  23.0   64.0  0.79   \n",
       "P230_V9_07102019     230        V9  07102019  11.0  0.25   5.0   12.0  0.05   \n",
       "\n",
       "                  PRP (Hib)     PCV ST1  ... median_mmNorm  \\\n",
       "P251_V9_09092019       7.35   26.071367  ...      0.045279   \n",
       "P261_V12_10222020      1.05   31.157284  ...       0.03523   \n",
       "P226_V12_09302020      3.57   23.329774  ...      0.082202   \n",
       "P210_V5_05062018       2.58  156.531647  ...      0.083593   \n",
       "P230_V9_07102019        0.3   81.383885  ...      0.048516   \n",
       "\n",
       "                  median_mmNorm_DTAPHib median_mmNorm_PCV PT_protected  \\\n",
       "P251_V9_09092019               0.013593          0.113246        False   \n",
       "P261_V12_10222020              0.102296          0.030523         True   \n",
       "P226_V12_09302020              0.097174          0.057712         True   \n",
       "P210_V5_05062018               0.115757          0.063515         True   \n",
       "P230_V9_07102019                0.02296          0.088023         True   \n",
       "\n",
       "                  Dip_protected FHA_protected PRN_protected TET_protected  \\\n",
       "P251_V9_09092019           True         False         False          True   \n",
       "P261_V12_10222020          True         False          True          True   \n",
       "P226_V12_09302020          True          True          True          True   \n",
       "P210_V5_05062018           True          True          True          True   \n",
       "P230_V9_07102019           True         False          True         False   \n",
       "\n",
       "                  PRP (Hib)_protected VR_group  \n",
       "P251_V9_09092019                 True      NVR  \n",
       "P261_V12_10222020                True      NVR  \n",
       "P226_V12_09302020                True      NVR  \n",
       "P210_V5_05062018                 True      NVR  \n",
       "P230_V9_07102019                 True      NVR  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_PCV = meta_matched.loc[~pd.isna(meta_matched['median_mmNorm_PCV'])]\n",
    "meta_PCV.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "73ad9121-4bfb-4ed4-928a-99805446b9c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>0.584018</td>\n",
       "      <td>0.001103</td>\n",
       "      <td>0.122408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>0.506911</td>\n",
       "      <td>0.005906</td>\n",
       "      <td>0.327790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <td>-0.415435</td>\n",
       "      <td>0.027913</td>\n",
       "      <td>0.811344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>0.407772</td>\n",
       "      <td>0.031243</td>\n",
       "      <td>0.811344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uridine</th>\n",
       "      <td>-0.396825</td>\n",
       "      <td>0.036547</td>\n",
       "      <td>0.811344</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     rho   p_value     p_adj\n",
       "5-HIAA          0.584018  0.001103  0.122408\n",
       "Guanosine       0.506911  0.005906  0.327790\n",
       "Nicotinic acid -0.415435  0.027913  0.811344\n",
       "Inosine         0.407772  0.031243  0.811344\n",
       "Uridine        -0.396825  0.036547  0.811344"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_PCV_v5 = meta_PCV.query(\"VisitCode == 'V5'\")\n",
    "metab_data_PCV_v5 = metab_data.loc[meta_PCV_v5.index].transpose()\n",
    "metab_data_PCV_v5 = metab_data_PCV_v5.loc[(metab_data_PCV_v5 > 0).sum(axis=1) > metab_data_PCV_v5.shape[1]*.2]\n",
    "v5_PCV_correlations = metab_data_PCV_v5.transpose().apply(spearmanr, b=meta_PCV_v5['median_mmNorm_PCV']).transpose()\n",
    "v5_PCV_correlations.columns = ['rho', 'p_value']\n",
    "v5_PCV_correlations['p_adj'] = p_adjust(v5_PCV_correlations['p_value'])\n",
    "v5_PCV_correlations = v5_PCV_correlations.sort_values('p_value')\n",
    "v5_PCV_correlations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1fced1-6d9f-47ea-bc7f-d0424062877b",
   "metadata": {},
   "source": [
    "Only one significantly correlated with PCV and it's our friend Phenylpyruvic acid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "24cd62d9-cd39-48cd-bdd3-2aa157492823",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metab_data_PCV_v5_w_titer = metab_data_PCV_v5.copy().transpose()\n",
    "metab_data_PCV_v5_w_titer['median_mmNorm_PCV'] = meta_v5.loc[metab_data_PCV_v5_w_titer.index, 'median_mmNorm_PCV']\n",
    "# metab_data_PCV_v5_w_titer\n",
    "_ = sns.scatterplot(x='Phenylpyruvic acid', y='median_mmNorm_PCV', data=metab_data_PCV_v5_w_titer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9bc3e0d-e12c-47ed-8b38-4ed6e8115765",
   "metadata": {},
   "source": [
    "Different compound same curve."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b15447c4-ecd4-4da1-92cc-a84fa311cc98",
   "metadata": {},
   "source": [
    "### 12 month DTAPHib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "93a5a51b-559b-4536-b7e7-0cbd9db55912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L-Tryptophan</th>\n",
       "      <td>-0.295164</td>\n",
       "      <td>0.076140</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate</th>\n",
       "      <td>0.279753</td>\n",
       "      <td>0.093554</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creatinine</th>\n",
       "      <td>0.242072</td>\n",
       "      <td>0.148880</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7oxoCA</th>\n",
       "      <td>-0.237790</td>\n",
       "      <td>0.156425</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Succinic acid</th>\n",
       "      <td>-0.237079</td>\n",
       "      <td>0.157704</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oxamic acid</th>\n",
       "      <td>-0.227359</td>\n",
       "      <td>0.175958</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-Hydroxybenzoic acid</th>\n",
       "      <td>-0.198909</td>\n",
       "      <td>0.237909</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Aspartic Acid</th>\n",
       "      <td>-0.195116</td>\n",
       "      <td>0.247160</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Threonine</th>\n",
       "      <td>-0.195116</td>\n",
       "      <td>0.247160</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>-0.192271</td>\n",
       "      <td>0.254255</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <td>0.189082</td>\n",
       "      <td>0.262368</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCDCA</th>\n",
       "      <td>-0.187767</td>\n",
       "      <td>0.265763</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Glutamine</th>\n",
       "      <td>-0.187055</td>\n",
       "      <td>0.267611</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indoxyl sulfate-13C6</th>\n",
       "      <td>0.186818</td>\n",
       "      <td>0.268229</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UDCA or HDCA</th>\n",
       "      <td>0.185870</td>\n",
       "      <td>0.270709</td>\n",
       "      <td>0.998889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            rho   p_value     p_adj\n",
       "L-Tryptophan          -0.295164  0.076140  0.998889\n",
       "p-cresol sulfate       0.279753  0.093554  0.998889\n",
       "Creatinine             0.242072  0.148880  0.998889\n",
       "7oxoCA                -0.237790  0.156425  0.998889\n",
       "Succinic acid         -0.237079  0.157704  0.998889\n",
       "Oxamic acid           -0.227359  0.175958  0.998889\n",
       "4-Hydroxybenzoic acid -0.198909  0.237909  0.998889\n",
       "L-Aspartic Acid       -0.195116  0.247160  0.998889\n",
       "L-Threonine           -0.195116  0.247160  0.998889\n",
       "Guanosine             -0.192271  0.254255  0.998889\n",
       "2-Deoxyuridine         0.189082  0.262368  0.998889\n",
       "GCDCA                 -0.187767  0.265763  0.998889\n",
       "L-Glutamine           -0.187055  0.267611  0.998889\n",
       "indoxyl sulfate-13C6   0.186818  0.268229  0.998889\n",
       "UDCA or HDCA           0.185870  0.270709  0.998889"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v9_DTAPHib_correlations = metab_data_v9.apply(spearmanr, b=meta_v9['median_mmNorm_DTAPHib']).transpose()\n",
    "v9_DTAPHib_correlations.columns = ['rho', 'p_value']\n",
    "v9_DTAPHib_correlations['p_adj'] = p_adjust(v9_DTAPHib_correlations['p_value'])\n",
    "v9_DTAPHib_correlations = v9_DTAPHib_correlations.sort_values('p_value')\n",
    "v9_DTAPHib_correlations.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c7d1d5-970b-415c-8d74-c8291c34bc7f",
   "metadata": {},
   "source": [
    "Lots significant again. I don't trust these results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2089ea63-41ef-40c4-a7ac-2ffe924085ef",
   "metadata": {},
   "source": [
    "### 12 month PCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "26b400e9-1a3b-42eb-9be8-52d92ba6231b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>trans-4-Hydroxy-L-proline</th>\n",
       "      <td>-0.566382</td>\n",
       "      <td>0.000258</td>\n",
       "      <td>0.028639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Threonine</th>\n",
       "      <td>-0.433618</td>\n",
       "      <td>0.007338</td>\n",
       "      <td>0.302729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>-0.422712</td>\n",
       "      <td>0.009148</td>\n",
       "      <td>0.302729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetyl D-galactosamine</th>\n",
       "      <td>-0.413752</td>\n",
       "      <td>0.010909</td>\n",
       "      <td>0.302729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-tryptophan-D5</th>\n",
       "      <td>0.368184</td>\n",
       "      <td>0.024953</td>\n",
       "      <td>0.553965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetyl-alpha-D-glucosamine 1-phosphate</th>\n",
       "      <td>-0.338549</td>\n",
       "      <td>0.040404</td>\n",
       "      <td>0.683281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Citrulline</th>\n",
       "      <td>-0.306069</td>\n",
       "      <td>0.065426</td>\n",
       "      <td>0.683281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetylneuraminic acid</th>\n",
       "      <td>-0.302750</td>\n",
       "      <td>0.068552</td>\n",
       "      <td>0.683281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xanthurenic acid</th>\n",
       "      <td>-0.299905</td>\n",
       "      <td>0.071324</td>\n",
       "      <td>0.683281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Carbamoyl-DL-aspartic acid</th>\n",
       "      <td>-0.298026</td>\n",
       "      <td>0.073203</td>\n",
       "      <td>0.683281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homocitrate</th>\n",
       "      <td>0.297842</td>\n",
       "      <td>0.073389</td>\n",
       "      <td>0.683281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Proline</th>\n",
       "      <td>-0.295875</td>\n",
       "      <td>0.075402</td>\n",
       "      <td>0.683281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Histidine</th>\n",
       "      <td>-0.291506</td>\n",
       "      <td>0.080024</td>\n",
       "      <td>0.683281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <td>-0.269963</td>\n",
       "      <td>0.106111</td>\n",
       "      <td>0.753085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-asparagine</th>\n",
       "      <td>-0.262447</td>\n",
       "      <td>0.116588</td>\n",
       "      <td>0.753085</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               rho   p_value     p_adj\n",
       "trans-4-Hydroxy-L-proline                -0.566382  0.000258  0.028639\n",
       "L-Threonine                              -0.433618  0.007338  0.302729\n",
       "L-Serine                                 -0.422712  0.009148  0.302729\n",
       "N-Acetyl D-galactosamine                 -0.413752  0.010909  0.302729\n",
       "L-tryptophan-D5                           0.368184  0.024953  0.553965\n",
       "N-Acetyl-alpha-D-glucosamine 1-phosphate -0.338549  0.040404  0.683281\n",
       "L-Citrulline                             -0.306069  0.065426  0.683281\n",
       "N-Acetylneuraminic acid                  -0.302750  0.068552  0.683281\n",
       "xanthurenic acid                         -0.299905  0.071324  0.683281\n",
       "N-Carbamoyl-DL-aspartic acid             -0.298026  0.073203  0.683281\n",
       "Homocitrate                               0.297842  0.073389  0.683281\n",
       "L-Proline                                -0.295875  0.075402  0.683281\n",
       "L-Histidine                              -0.291506  0.080024  0.683281\n",
       "Nicotinic acid                           -0.269963  0.106111  0.753085\n",
       "L-asparagine                             -0.262447  0.116588  0.753085"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_PCV_v9 = meta_PCV.query(\"VisitCode == 'V9'\")\n",
    "metab_data_PCV_v9 = metab_data.loc[meta_PCV_v9.index].transpose()\n",
    "metab_data_PCV_v9 = metab_data_PCV_v9.loc[(metab_data_PCV_v9 > 0).sum(axis=1) > metab_data_PCV_v9.shape[1]*.2]\n",
    "v9_PCV_correlations = metab_data_PCV_v9.transpose().apply(spearmanr, b=meta_PCV_v9['median_mmNorm_PCV']).transpose()\n",
    "v9_PCV_correlations.columns = ['rho', 'p_value']\n",
    "v9_PCV_correlations['p_adj'] = p_adjust(v9_PCV_correlations['p_value'])\n",
    "v9_PCV_correlations = v9_PCV_correlations.sort_values('p_value')\n",
    "v9_PCV_correlations.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb41fe42-a959-4fd7-9758-b18085c00c75",
   "metadata": {},
   "source": [
    "One significant metabolite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "45df39a4-cf34-4f23-b541-7b82f29ed17f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metab_data_PCV_v9_w_titer = metab_data_PCV_v9.copy().transpose()\n",
    "metab_data_PCV_v9_w_titer['median_mmNorm_PCV'] = meta_v9.loc[metab_data_PCV_v9_w_titer.index, 'median_mmNorm_PCV']\n",
    "_ = sns.scatterplot(x='trans-4-Hydroxy-L-proline', y='median_mmNorm_PCV', data=metab_data_PCV_v9_w_titer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "616f4d66-23f6-463a-807a-9a07c6969ac5",
   "metadata": {},
   "source": [
    "## Correlate relative abundance with separate median titers\n",
    "\n",
    "Now we will do the same thing but with relative abundance for PCV and DTAPHib."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c30a3401-77ae-4524-9dd5-25dad23b2f1b",
   "metadata": {},
   "source": [
    "### 2 month DTAPHib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a78de7cb-fb53-4a13-94c2-78a282e5f7c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p-hydroxy-hippuric acid</th>\n",
       "      <td>0.446634</td>\n",
       "      <td>0.017191</td>\n",
       "      <td>0.924327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Leucine</th>\n",
       "      <td>0.412151</td>\n",
       "      <td>0.029303</td>\n",
       "      <td>0.924327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Isoleucine</th>\n",
       "      <td>0.402299</td>\n",
       "      <td>0.033811</td>\n",
       "      <td>0.924327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-asparagine</th>\n",
       "      <td>0.400657</td>\n",
       "      <td>0.034614</td>\n",
       "      <td>0.924327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hippurate</th>\n",
       "      <td>0.384784</td>\n",
       "      <td>0.043191</td>\n",
       "      <td>0.924327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              rho   p_value     p_adj\n",
       "p-hydroxy-hippuric acid  0.446634  0.017191  0.924327\n",
       "L-Leucine                0.412151  0.029303  0.924327\n",
       "L-Isoleucine             0.402299  0.033811  0.924327\n",
       "L-asparagine             0.400657  0.034614  0.924327\n",
       "hippurate                0.384784  0.043191  0.924327"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v5_DTAPHib_correlations_rel = metab_data_rel_v5.apply(spearmanr, b=meta_v5['median_mmNorm_DTAPHib']).transpose()\n",
    "v5_DTAPHib_correlations_rel.columns = ['rho', 'p_value']\n",
    "v5_DTAPHib_correlations_rel['p_adj'] = p_adjust(v5_DTAPHib_correlations_rel['p_value'])\n",
    "v5_DTAPHib_correlations_rel = v5_DTAPHib_correlations_rel.sort_values('p_value')\n",
    "v5_DTAPHib_correlations_rel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7d0ce93-b31d-48f4-8faa-0bd7c5ee0032",
   "metadata": {},
   "source": [
    "Same three compounds."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883f99a3-58b9-459b-9994-8cf752d3565d",
   "metadata": {},
   "source": [
    "### 2 month PCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fb1a01f1-080f-43ac-9c40-f62eae38f63f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>0.563218</td>\n",
       "      <td>0.001805</td>\n",
       "      <td>0.200342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>0.515052</td>\n",
       "      <td>0.005037</td>\n",
       "      <td>0.279541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uridine</th>\n",
       "      <td>-0.472359</td>\n",
       "      <td>0.011146</td>\n",
       "      <td>0.412386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <td>-0.437329</td>\n",
       "      <td>0.019954</td>\n",
       "      <td>0.553731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>0.411604</td>\n",
       "      <td>0.029540</td>\n",
       "      <td>0.625508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     rho   p_value     p_adj\n",
       "5-HIAA          0.563218  0.001805  0.200342\n",
       "Guanosine       0.515052  0.005037  0.279541\n",
       "Uridine        -0.472359  0.011146  0.412386\n",
       "Nicotinic acid -0.437329  0.019954  0.553731\n",
       "Inosine         0.411604  0.029540  0.625508"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_PCV_rel_v5 = metab_data_rel.loc[meta_PCV_v5.index].transpose()\n",
    "v5_PCV_correlations_rel = metab_data_PCV_rel_v5.transpose().apply(spearmanr, b=meta_PCV_v5['median_mmNorm_PCV']).transpose()\n",
    "v5_PCV_correlations_rel.columns = ['rho', 'p_value']\n",
    "v5_PCV_correlations_rel['p_adj'] = p_adjust(v5_PCV_correlations_rel['p_value'])\n",
    "v5_PCV_correlations_rel = v5_PCV_correlations_rel.sort_values('p_value')\n",
    "v5_PCV_correlations_rel.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06e1bb31-3653-4679-a5ae-7dc9fdd6ee0c",
   "metadata": {},
   "source": [
    "Just phenylpyruvic acid."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c929d6ef-4122-410f-96c0-2a3e05173545",
   "metadata": {},
   "source": [
    "### 12 month DTAPHib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3622ef79-bdef-4be1-a147-6815e10aab65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L-Tryptophan</th>\n",
       "      <td>-0.315552</td>\n",
       "      <td>0.057109</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate</th>\n",
       "      <td>0.293741</td>\n",
       "      <td>0.077632</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Aspartic Acid</th>\n",
       "      <td>-0.287103</td>\n",
       "      <td>0.084903</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creatinine</th>\n",
       "      <td>0.232101</td>\n",
       "      <td>0.166874</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-phenyllactic acid</th>\n",
       "      <td>0.228070</td>\n",
       "      <td>0.174574</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7oxoCA</th>\n",
       "      <td>-0.225699</td>\n",
       "      <td>0.179220</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Threonine</th>\n",
       "      <td>-0.219298</td>\n",
       "      <td>0.192201</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-tryptophan-D5</th>\n",
       "      <td>-0.212423</td>\n",
       "      <td>0.206865</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oxamic acid</th>\n",
       "      <td>-0.198672</td>\n",
       "      <td>0.238480</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UDCA or HDCA</th>\n",
       "      <td>0.196064</td>\n",
       "      <td>0.244825</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xanthurenic acid</th>\n",
       "      <td>-0.190138</td>\n",
       "      <td>0.259664</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCDCA</th>\n",
       "      <td>-0.188004</td>\n",
       "      <td>0.265149</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanillic acid</th>\n",
       "      <td>0.182314</td>\n",
       "      <td>0.280145</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Glutamine</th>\n",
       "      <td>-0.174016</td>\n",
       "      <td>0.302985</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-hydroxyphenylacetate</th>\n",
       "      <td>-0.170460</td>\n",
       "      <td>0.313125</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Succinic acid</th>\n",
       "      <td>-0.166430</td>\n",
       "      <td>0.324873</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCDCA</th>\n",
       "      <td>-0.166193</td>\n",
       "      <td>0.325572</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DCA</th>\n",
       "      <td>0.162912</td>\n",
       "      <td>0.335347</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <td>0.162873</td>\n",
       "      <td>0.335463</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <td>-0.157421</td>\n",
       "      <td>0.352109</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orotic acid</th>\n",
       "      <td>-0.153627</td>\n",
       "      <td>0.363978</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Carnitine</th>\n",
       "      <td>0.146989</td>\n",
       "      <td>0.385317</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.143433</td>\n",
       "      <td>0.397043</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>-0.142959</td>\n",
       "      <td>0.398621</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kynurenic acid</th>\n",
       "      <td>-0.135846</td>\n",
       "      <td>0.422734</td>\n",
       "      <td>0.994404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             rho   p_value     p_adj\n",
       "L-Tryptophan           -0.315552  0.057109  0.994404\n",
       "p-cresol sulfate        0.293741  0.077632  0.994404\n",
       "L-Aspartic Acid        -0.287103  0.084903  0.994404\n",
       "Creatinine              0.232101  0.166874  0.994404\n",
       "3-phenyllactic acid     0.228070  0.174574  0.994404\n",
       "7oxoCA                 -0.225699  0.179220  0.994404\n",
       "L-Threonine            -0.219298  0.192201  0.994404\n",
       "L-tryptophan-D5        -0.212423  0.206865  0.994404\n",
       "Oxamic acid            -0.198672  0.238480  0.994404\n",
       "UDCA or HDCA            0.196064  0.244825  0.994404\n",
       "xanthurenic acid       -0.190138  0.259664  0.994404\n",
       "GCDCA                  -0.188004  0.265149  0.994404\n",
       "Vanillic acid           0.182314  0.280145  0.994404\n",
       "L-Glutamine            -0.174016  0.302985  0.994404\n",
       "4-hydroxyphenylacetate -0.170460  0.313125  0.994404\n",
       "Succinic acid          -0.166430  0.324873  0.994404\n",
       "TCDCA                  -0.166193  0.325572  0.994404\n",
       "DCA                     0.162912  0.335347  0.994404\n",
       "2-Deoxyuridine          0.162873  0.335463  0.994404\n",
       "Nicotinic acid         -0.157421  0.352109  0.994404\n",
       "Orotic acid            -0.153627  0.363978  0.994404\n",
       "L-Carnitine             0.146989  0.385317  0.994404\n",
       "serotonin               0.143433  0.397043  0.994404\n",
       "Guanosine              -0.142959  0.398621  0.994404\n",
       "kynurenic acid         -0.135846  0.422734  0.994404"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v9_DTAPHib_correlations_rel = metab_data_rel_v9.apply(spearmanr, b=meta_v9['median_mmNorm_DTAPHib']).transpose()\n",
    "v9_DTAPHib_correlations_rel.columns = ['rho', 'p_value']\n",
    "v9_DTAPHib_correlations_rel['p_adj'] = p_adjust(v9_DTAPHib_correlations_rel['p_value'])\n",
    "v9_DTAPHib_correlations_rel = v9_DTAPHib_correlations_rel.sort_values('p_value')\n",
    "v9_DTAPHib_correlations_rel.head(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b36a48-0582-497e-aaaf-b3dbb8a0b9f2",
   "metadata": {},
   "source": [
    "Lots significant again. I don't trust these results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1679ceae-f15f-4216-881c-693ccb28fdb9",
   "metadata": {},
   "source": [
    "### 12 month PCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2136e5fa-46c7-448f-811f-c26325bab273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>trans-4-Hydroxy-L-proline</th>\n",
       "      <td>-0.521811</td>\n",
       "      <td>0.000925</td>\n",
       "      <td>0.102722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetyl D-galactosamine</th>\n",
       "      <td>-0.429113</td>\n",
       "      <td>0.008044</td>\n",
       "      <td>0.409413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>-0.410621</td>\n",
       "      <td>0.011589</td>\n",
       "      <td>0.409413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetyl-alpha-D-glucosamine 1-phosphate</th>\n",
       "      <td>-0.397819</td>\n",
       "      <td>0.014754</td>\n",
       "      <td>0.409413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Threonine</th>\n",
       "      <td>-0.365813</td>\n",
       "      <td>0.025975</td>\n",
       "      <td>0.576636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetylneuraminic acid</th>\n",
       "      <td>-0.334282</td>\n",
       "      <td>0.043160</td>\n",
       "      <td>0.603896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xanthurenic acid</th>\n",
       "      <td>-0.330251</td>\n",
       "      <td>0.045902</td>\n",
       "      <td>0.603896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-3-Dihydroxyisovalerate</th>\n",
       "      <td>0.329777</td>\n",
       "      <td>0.046233</td>\n",
       "      <td>0.603896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glyceric acid</th>\n",
       "      <td>-0.322902</td>\n",
       "      <td>0.051260</td>\n",
       "      <td>0.603896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Homocitrate</th>\n",
       "      <td>0.318872</td>\n",
       "      <td>0.054405</td>\n",
       "      <td>0.603896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Glutamine</th>\n",
       "      <td>-0.308914</td>\n",
       "      <td>0.062836</td>\n",
       "      <td>0.634073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate</th>\n",
       "      <td>0.298720</td>\n",
       "      <td>0.072505</td>\n",
       "      <td>0.670674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha-D(+)Mannose 1-phosphate</th>\n",
       "      <td>-0.270744</td>\n",
       "      <td>0.105064</td>\n",
       "      <td>0.733035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               rho   p_value     p_adj\n",
       "trans-4-Hydroxy-L-proline                -0.521811  0.000925  0.102722\n",
       "N-Acetyl D-galactosamine                 -0.429113  0.008044  0.409413\n",
       "L-Serine                                 -0.410621  0.011589  0.409413\n",
       "N-Acetyl-alpha-D-glucosamine 1-phosphate -0.397819  0.014754  0.409413\n",
       "L-Threonine                              -0.365813  0.025975  0.576636\n",
       "N-Acetylneuraminic acid                  -0.334282  0.043160  0.603896\n",
       "xanthurenic acid                         -0.330251  0.045902  0.603896\n",
       "2-3-Dihydroxyisovalerate                  0.329777  0.046233  0.603896\n",
       "Glyceric acid                            -0.322902  0.051260  0.603896\n",
       "Homocitrate                               0.318872  0.054405  0.603896\n",
       "L-Glutamine                              -0.308914  0.062836  0.634073\n",
       "p-cresol sulfate                          0.298720  0.072505  0.670674\n",
       "alpha-D(+)Mannose 1-phosphate            -0.270744  0.105064  0.733035"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_PCV_rel_v9 = metab_data_rel.loc[meta_PCV_v9.index].transpose()\n",
    "v9_PCV_correlations_rel = metab_data_PCV_rel_v9.transpose().apply(spearmanr, b=meta_PCV_v9['median_mmNorm_PCV']).transpose()\n",
    "v9_PCV_correlations_rel.columns = ['rho', 'p_value']\n",
    "v9_PCV_correlations_rel['p_adj'] = p_adjust(v9_PCV_correlations_rel['p_value'])\n",
    "v9_PCV_correlations_rel = v9_PCV_correlations_rel.sort_values('p_value')\n",
    "v9_PCV_correlations_rel.head(13)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "310c778e-fe9f-4346-abca-3b8da4f1c981",
   "metadata": {},
   "source": [
    "Lots significant again. I don't trust these results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ec7cfd-336f-4b8e-9e58-0f3f7ca9553a",
   "metadata": {},
   "source": [
    "## Correlate CLR with separate median titers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40408565-e8a1-4c6d-9f50-ac79a74835e5",
   "metadata": {},
   "source": [
    "### 2 month correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "756ab0fb-275f-41f3-8bd8-392824e76202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>kynurenic acid</th>\n",
       "      <td>-0.452655</td>\n",
       "      <td>0.015577</td>\n",
       "      <td>0.993383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-asparagine</th>\n",
       "      <td>0.394089</td>\n",
       "      <td>0.037980</td>\n",
       "      <td>0.993383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Melibiose</th>\n",
       "      <td>0.382047</td>\n",
       "      <td>0.044827</td>\n",
       "      <td>0.993383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-hydroxy-hippuric acid</th>\n",
       "      <td>0.364532</td>\n",
       "      <td>0.056496</td>\n",
       "      <td>0.993383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Leucine</th>\n",
       "      <td>0.353585</td>\n",
       "      <td>0.064916</td>\n",
       "      <td>0.993383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>-0.349206</td>\n",
       "      <td>0.068545</td>\n",
       "      <td>0.993383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanillic acid</th>\n",
       "      <td>0.305966</td>\n",
       "      <td>0.113319</td>\n",
       "      <td>0.993383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alpha-D(+)Mannose 1-phosphate</th>\n",
       "      <td>0.291188</td>\n",
       "      <td>0.132744</td>\n",
       "      <td>0.993383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    rho   p_value     p_adj\n",
       "kynurenic acid                -0.452655  0.015577  0.993383\n",
       "L-asparagine                   0.394089  0.037980  0.993383\n",
       "Melibiose                      0.382047  0.044827  0.993383\n",
       "p-hydroxy-hippuric acid        0.364532  0.056496  0.993383\n",
       "L-Leucine                      0.353585  0.064916  0.993383\n",
       "Inosine                       -0.349206  0.068545  0.993383\n",
       "Vanillic acid                  0.305966  0.113319  0.993383\n",
       "alpha-D(+)Mannose 1-phosphate  0.291188  0.132744  0.993383"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v5_DTAPHib_correlations_clr = metab_data_clr_v5.apply(spearmanr, b=meta_v5['median_mmNorm_DTAPHib']).transpose()\n",
    "v5_DTAPHib_correlations_clr.columns = ['rho', 'p_value']\n",
    "v5_DTAPHib_correlations_clr['p_adj'] = p_adjust(v5_DTAPHib_correlations_clr['p_value'])\n",
    "v5_DTAPHib_correlations_clr = v5_DTAPHib_correlations_clr.sort_values('p_value')\n",
    "v5_DTAPHib_correlations_clr.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ff5061b1-a8ad-4fe3-ac28-4ab79b66ddbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5-HIAA</th>\n",
       "      <td>0.577449</td>\n",
       "      <td>0.001293</td>\n",
       "      <td>0.143518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guanosine</th>\n",
       "      <td>0.501916</td>\n",
       "      <td>0.006500</td>\n",
       "      <td>0.360755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inosine</th>\n",
       "      <td>0.427477</td>\n",
       "      <td>0.023268</td>\n",
       "      <td>0.860900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <td>-0.391899</td>\n",
       "      <td>0.039158</td>\n",
       "      <td>0.912747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.341544</td>\n",
       "      <td>0.075271</td>\n",
       "      <td>0.912747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     rho   p_value     p_adj\n",
       "5-HIAA          0.577449  0.001293  0.143518\n",
       "Guanosine       0.501916  0.006500  0.360755\n",
       "Inosine         0.427477  0.023268  0.860900\n",
       "Nicotinic acid -0.391899  0.039158  0.912747\n",
       "serotonin       0.341544  0.075271  0.912747"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_PCV_clr_v5 = metab_data_clr.loc[meta_PCV_v5.index].transpose()\n",
    "v5_PCV_correlations_clr = metab_data_PCV_clr_v5.transpose().apply(spearmanr, b=meta_PCV_v5['median_mmNorm_PCV']).transpose()\n",
    "v5_PCV_correlations_clr.columns = ['rho', 'p_value']\n",
    "v5_PCV_correlations_clr['p_adj'] = p_adjust(v5_PCV_correlations_clr['p_value'])\n",
    "v5_PCV_correlations_clr = v5_PCV_correlations_clr.sort_values('p_value')\n",
    "v5_PCV_correlations_clr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f9d8744-de59-4198-bc94-bbd971ff2152",
   "metadata": {},
   "source": [
    "Same results as all other 2 month correlations for DTAPHib and PCV."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e0377bb-0877-4036-a11e-d5e45d4ae158",
   "metadata": {},
   "source": [
    "### 12 month correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9562e912-fbb0-4044-af69-fcc589de5d63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L-Aspartic Acid</th>\n",
       "      <td>-0.318160</td>\n",
       "      <td>0.054976</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate</th>\n",
       "      <td>0.297060</td>\n",
       "      <td>0.074184</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Tryptophan</th>\n",
       "      <td>-0.293741</td>\n",
       "      <td>0.077632</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-phenyllactic acid</th>\n",
       "      <td>0.282598</td>\n",
       "      <td>0.090129</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Threonine</th>\n",
       "      <td>-0.238739</td>\n",
       "      <td>0.154730</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UDCA or HDCA</th>\n",
       "      <td>0.229018</td>\n",
       "      <td>0.172740</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7oxoCA</th>\n",
       "      <td>-0.224040</td>\n",
       "      <td>0.182524</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Creatinine</th>\n",
       "      <td>0.222617</td>\n",
       "      <td>0.185390</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Glutamine</th>\n",
       "      <td>-0.215505</td>\n",
       "      <td>0.200198</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanillic acid</th>\n",
       "      <td>0.210763</td>\n",
       "      <td>0.210518</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kynurenic acid</th>\n",
       "      <td>-0.196776</td>\n",
       "      <td>0.243084</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Xanthosine</th>\n",
       "      <td>-0.189426</td>\n",
       "      <td>0.261484</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Proline</th>\n",
       "      <td>-0.189189</td>\n",
       "      <td>0.262092</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xanthurenic acid</th>\n",
       "      <td>-0.184922</td>\n",
       "      <td>0.273205</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orotic acid</th>\n",
       "      <td>-0.184685</td>\n",
       "      <td>0.273831</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxyuridine</th>\n",
       "      <td>0.182077</td>\n",
       "      <td>0.280782</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCDCA</th>\n",
       "      <td>-0.180180</td>\n",
       "      <td>0.285909</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GCDCA</th>\n",
       "      <td>-0.175439</td>\n",
       "      <td>0.298988</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DCA</th>\n",
       "      <td>0.174727</td>\n",
       "      <td>0.300982</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4-hydroxyphenylacetate</th>\n",
       "      <td>-0.160503</td>\n",
       "      <td>0.342640</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nicotinic acid</th>\n",
       "      <td>-0.157895</td>\n",
       "      <td>0.350642</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>serotonin</th>\n",
       "      <td>0.145330</td>\n",
       "      <td>0.390764</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCA</th>\n",
       "      <td>-0.143670</td>\n",
       "      <td>0.396255</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TUDCA</th>\n",
       "      <td>0.143670</td>\n",
       "      <td>0.396255</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Arabinose</th>\n",
       "      <td>0.141062</td>\n",
       "      <td>0.404973</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-Deoxycytidine 5-diphosphate</th>\n",
       "      <td>0.139403</td>\n",
       "      <td>0.410577</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Succinic acid</th>\n",
       "      <td>-0.139403</td>\n",
       "      <td>0.410577</td>\n",
       "      <td>0.983307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    rho   p_value     p_adj\n",
       "L-Aspartic Acid               -0.318160  0.054976  0.983307\n",
       "p-cresol sulfate               0.297060  0.074184  0.983307\n",
       "L-Tryptophan                  -0.293741  0.077632  0.983307\n",
       "3-phenyllactic acid            0.282598  0.090129  0.983307\n",
       "L-Threonine                   -0.238739  0.154730  0.983307\n",
       "UDCA or HDCA                   0.229018  0.172740  0.983307\n",
       "7oxoCA                        -0.224040  0.182524  0.983307\n",
       "Creatinine                     0.222617  0.185390  0.983307\n",
       "L-Glutamine                   -0.215505  0.200198  0.983307\n",
       "Vanillic acid                  0.210763  0.210518  0.983307\n",
       "kynurenic acid                -0.196776  0.243084  0.983307\n",
       "Xanthosine                    -0.189426  0.261484  0.983307\n",
       "L-Proline                     -0.189189  0.262092  0.983307\n",
       "xanthurenic acid              -0.184922  0.273205  0.983307\n",
       "Orotic acid                   -0.184685  0.273831  0.983307\n",
       "2-Deoxyuridine                 0.182077  0.280782  0.983307\n",
       "TCDCA                         -0.180180  0.285909  0.983307\n",
       "GCDCA                         -0.175439  0.298988  0.983307\n",
       "DCA                            0.174727  0.300982  0.983307\n",
       "4-hydroxyphenylacetate        -0.160503  0.342640  0.983307\n",
       "Nicotinic acid                -0.157895  0.350642  0.983307\n",
       "serotonin                      0.145330  0.390764  0.983307\n",
       "TCA                           -0.143670  0.396255  0.983307\n",
       "TUDCA                          0.143670  0.396255  0.983307\n",
       "L-Arabinose                    0.141062  0.404973  0.983307\n",
       "2-Deoxycytidine 5-diphosphate  0.139403  0.410577  0.983307\n",
       "Succinic acid                 -0.139403  0.410577  0.983307"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v9_DTAPHib_correlations_clr = metab_data_clr_v9.apply(spearmanr, b=meta_v9['median_mmNorm_DTAPHib']).transpose()\n",
    "v9_DTAPHib_correlations_clr.columns = ['rho', 'p_value']\n",
    "v9_DTAPHib_correlations_clr['p_adj'] = p_adjust(v9_DTAPHib_correlations_clr['p_value'])\n",
    "v9_DTAPHib_correlations_clr = v9_DTAPHib_correlations_clr.sort_values('p_value')\n",
    "v9_DTAPHib_correlations_clr.head(27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "07a7c9be-034b-4a8e-9755-0d4251fa0100",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rho</th>\n",
       "      <th>p_value</th>\n",
       "      <th>p_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L-Serine</th>\n",
       "      <td>-0.435752</td>\n",
       "      <td>0.007022</td>\n",
       "      <td>0.610403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trans-4-Hydroxy-L-proline</th>\n",
       "      <td>-0.401375</td>\n",
       "      <td>0.013809</td>\n",
       "      <td>0.610403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N-Acetyl D-galactosamine</th>\n",
       "      <td>-0.385965</td>\n",
       "      <td>0.018305</td>\n",
       "      <td>0.610403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L-Threonine</th>\n",
       "      <td>-0.375533</td>\n",
       "      <td>0.021997</td>\n",
       "      <td>0.610403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-cresol sulfate</th>\n",
       "      <td>0.354433</td>\n",
       "      <td>0.031369</td>\n",
       "      <td>0.696396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                rho   p_value     p_adj\n",
       "L-Serine                  -0.435752  0.007022  0.610403\n",
       "trans-4-Hydroxy-L-proline -0.401375  0.013809  0.610403\n",
       "N-Acetyl D-galactosamine  -0.385965  0.018305  0.610403\n",
       "L-Threonine               -0.375533  0.021997  0.610403\n",
       "p-cresol sulfate           0.354433  0.031369  0.696396"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metab_data_PCV_clr_v9 = metab_data_clr.loc[meta_PCV_v9.index].transpose()\n",
    "v9_PCV_correlations_clr = metab_data_PCV_clr_v9.transpose().apply(spearmanr, b=meta_PCV_v9['median_mmNorm_PCV']).transpose()\n",
    "v9_PCV_correlations_clr.columns = ['rho', 'p_value']\n",
    "v9_PCV_correlations_clr['p_adj'] = p_adjust(v9_PCV_correlations_clr['p_value'])\n",
    "v9_PCV_correlations_clr = v9_PCV_correlations_clr.sort_values('p_value')\n",
    "v9_PCV_correlations_clr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6f84b5-f5b3-4d3b-9323-e187909d79db",
   "metadata": {},
   "source": [
    "Lots significant again. I don't trust these results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b17ecd5-ad35-4b56-b7c8-4a2058e72aad",
   "metadata": {},
   "source": [
    "## Compare normalizations for split titers\n",
    "\n",
    "Build up some venn diagrams to compare normalizations for the split titers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dc17a4aa-3338-49a3-a0d8-6a893705a28b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shaffmic/miniconda3/envs/imc/lib/python3.9/site-packages/matplotlib_venn/_venn3.py:47: UserWarning: All circles have zero area\n",
      "  warnings.warn(\"All circles have zero area\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = venn3([set(v9_DTAPHib_correlations.query('p_adj < .05').index),\n",
    "           set(v9_DTAPHib_correlations_rel.query('p_adj < .05').index),\n",
    "           set(v9_DTAPHib_correlations_clr.query('p_adj < .05').index)], set_labels=('un-normalized',\n",
    "                                                                                     'relative abundance', 'CLR'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7b4f1b71-2cee-49bd-84be-30c1aa2fcfb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shaffmic/miniconda3/envs/imc/lib/python3.9/site-packages/matplotlib_venn/_venn3.py:57: UserWarning: Circle B has zero area\n",
      "  warnings.warn(\"Circle B has zero area\")\n",
      "/Users/shaffmic/miniconda3/envs/imc/lib/python3.9/site-packages/matplotlib_venn/_venn3.py:61: UserWarning: Circle C has zero area\n",
      "  warnings.warn(\"Circle C has zero area\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = venn3([set(v9_PCV_correlations.query('p_adj < .05').index),\n",
    "           set(v9_PCV_correlations_rel.query('p_adj < .05').index),\n",
    "           set(v9_PCV_correlations_clr.query('p_adj < .05').index)], set_labels=('un-normalized',\n",
    "                                                                                     'relative abundance', 'CLR'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "360f77fa-bcb3-4639-9ab0-224d474aa94d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
